Computer systems and methods for the query and visualization of multidimensional databases

ABSTRACT

In response to a user request, a computer generates a graphical user interface on a computer display. A schema information region of the graphical user interface includes multiple operand names, each operand name associated with one or more fields of a multi-dimensional database. A data visualization region of the graphical user interface includes multiple shelves. Upon detecting a user selection of the operand names and a user request to associate each user-selected operand name with a respective shelf in the data visualization region, the computer generates a visual table in the data visualization region in accordance with the associations between the operand names and the corresponding shelves. The visual table includes a plurality of panes, each pane having at least one axis defined based on data for the fields associated with a respective operand name.

CROSS REFERENCE TO RELATED APPLICATIONS

This Application is a continuation of U.S. patent application Ser. No.13/019,227, filed Feb. 1, 2011, now U.S. Pat. No. 8,140,586, which is acontinuation of U.S. patent application Ser. No. 12/777,172, filed May10, 2010, now U.S. Pat. No. 7,882,144, which is a continuation of U.S.application Ser. No. 11/488,407 filed Jul. 17, 2006, now U.S. Pat. No.7,716,173, which is a continuation of U.S. patent application Ser. No.10/453,834, filed Jun. 2, 2003, now U.S. Pat. No. 7,089,266, which areincorporated herein by reference in their entireties.

This invention was made with Government support under contractW-7405-ENG-48 awarded by the Department of Energy. The Government hascertain rights in this invention.

1. FIELD OF THE INVENTION

This invention relates generally to an interactive visual explorationtool that facilitates exploratory analysis of databases having ahierarchical structure.

2. BACKGROUND OF THE INVENTION

In the last several years, large databases have become common in avariety of applications. Corporations are creating large data warehousesof historical data on key aspects of their operations. Corporations arealso creating many small databases using desktop applications that arecreated to examine some specific aspect of their business. Internationalresearch projects such as the Human Genome Project and the Sloan DigitalSky Survey are generating massive scientific databases. One challengewith these databases is the extraction of meaning from the data theycontain: to discover structure, find patterns, and derive causalrelationships. The sheer size of these data sets complicates this task.Interactive calculations that require visiting each record are notplausible. It is also not feasible for an analyst to reason about orview the entire data set at its finest level of detail. Moreover, evenwhen the data sets are small, their complexity often makes it difficultto glean meaning without applying aggregations or creating simplifyingsummaries.

Imposing meaningful hierarchical structure on databases provides levelsof abstraction that can be leveraged by both the computer and theanalyst. These hierarchies can come from several different sources. Somehierarchies are provided by the inherent nature of the database. Datamining algorithms, such as decision trees and clustering techniques thatclassify the data and thereby automatically derive hierarchies can beused to determine database hierarchy. Part of the analysis task whendealing with automatically generated hierarchies is an understanding andtrusting the results. See, for example, 2001, Thearling et al.,“Visualizing Data Mining Models” in Information Visualization in DataMining and Knowledge Discovery, Fayyad, Grinstein and Wierse eds.,Morgan Kaufman, which is hereby incorporated by reference in itsentirety.

FIG. 9 illustrates the hierarchy for a time dimension 100. Within timedimension 100, there are four levels 110. They are “All”, year, quarter,and month. Simple hierarchies, like the one shown in FIG. 9, arecommonly modeled using a star schema. The entire dimension hierarchy isrepresented by a single dimension table joined to the base fact table.In this type of hierarchy, there is only one path of aggregation.However, there are more complex dimension hierarchies where theaggregation path can branch. For example, a time dimension mightaggregate from “day” to both week and “month”.

To provide another illustration of the concept of a star schema,consider the case in which one wishes to analyze monthly total productsales for a department store by breaking down the data by region andstore. Raw data can come in the form of product managers' (FIG. 1) andregional managers' (FIG. 2) quarterly sales reports. Once the data hasbeen collected and refined, it may reside in a large base table. Inaddition, there may be adjunct lookup tables. A star schema for thisbase data is shown in FIG. 3. The table schema of FIG. 3 is called astar schema because the central fact table is depicted as surrounded byeach of the dimension tables that describe each dimension. In thisexample, the base sales data table is the fact table and each lookuptable is a dimension table.

The stores, weeks, and products columns in the fact table in FIG. 3contain numeric values. Fact tables can grow to huge numbers of rows.The lookup tables contain hierarchy information relating each store,week, and product with its higher-level aggregations. For example store1 in the base table of FIG. 3 connects with the “Store Lookup” tablewhere it has the name Ridgewood and rolls up to the Northeast region.Product 2 in the base table connects with the “Product Lookup” tablewhere it has the name olive oil soap and rolls up into the product typesoap in the skin care products group. Thomsen, 1997, OLAP Solutions:Building Multidimensional Information Systems, Wiley ComputerPublishing, New York, which is hereby incorporated by reference in itsentirety.

The most common schemata found in databases are the star schema andsnowflake schema. Each schema has a fact table containing data items ofinterest (measures) in the analysis for which the database is built.These data items might be transaction amounts such as the amountinvested in a mutual fund or the profit on a sales transaction. The facttable is surrounded by dimension tables containing detailed informationused to summarize the fact table in different ways. An illustration of astar schema has been provided (FIG. 3). FIG. 4 illustrates a snowflakeschema that includes hierarchy. The snowflake and star schema provide aconceptual multidimensional view of the database. The database is a coreset of measures characterized by a number of dimensions rather than aset of interrelated tables. This organization correlates directly withthe typical analysis query that summarizes a few quantitative attributes(or measures) such as profit or sales by several characterizingattributes (or dimensions) such as product, location, or date over alarge number of triples. The primary differences between the star andsnowflake schema arise in how they model hierarchical structures on thedimensions.

When referring to values within a dimension hierarchy, a dotted notationcan be used to specify a specific path from the root level “All” (FIG.9) of the hierarchy down to the specified value. Specifically, to referto a value on level m of a hierarchy, the dimension name is firstoptionally listed, then zero or more of the (m−1) intermediate ancestorvalues, and then finally the value on the m^(th) level, all separated byperiods. For example, the Jan node on the Month level in the timehierarchy that corresponds to January, 1998, can be referred to as1998.Qtr1 Jan. When this notation is used, the reference is called aqualified value. When a value is simply described by its node value(without any path to the root node) the reference is called anunqualified value.

2.1 Types of Databases

One form of database is a relational warehouse, such as a structuredquery language (SQL) database. Relational warehouses organize data intotables. Each row in a table corresponds to a basic entity or fact andeach column represents a property of that entity. See, for example,Thomsen, 1997, OLAP Solutions: Building Multidimensional InformationSystems, Wiley Computer Publishing, New York. For example, a table mayrepresent transactions in a bank, where each row corresponds to a singletransaction. As such, each transaction may have multiple properties,such as the transaction amount, the account balance, the bank branch,and the customer. As used herein, a row in a table is referred to as atuple or record, and a column in the relation is referred to as a field.Such tables are also referred to as relations. As such, a relation isdefined as a database table that contains a set of tuples.

It is possible to create dimension tables and star schemas in relationalwarehouses. A single relational warehouse will contain manyheterogeneous but interrelated tables. The fields (columns) within atable can be partitioned into two types: dimensions and measures.Dimensions and measures are similar to independent and dependentvariables in traditional analysis. For example, the bank branch and thecustomer would be dimensions, while the account balance would be ameasure.

Multidimensional databases are structured as n-dimensional data cubes.Each dimension in the data cube corresponds to one dimension in therelational schema (e.g., in the star schema, snowflake schema etc.).Each cell in the data cube contains all the measures in the relationalschema corresponding to a unique combination of values for eachdimension. The dimensions within a data cube are often augmented with ahierarchical structure. This hierarchical structure can be derived fromthe semantic levels of detail within the dimension or generated fromclassification algorithms. Using these hierarchies, the analyst canexplore and analyze the data cube at multiple meaningful levels ofaggregation calculated from a base fact table (e.g., a relation in thedatabase with the raw data). Each cell in the data cube now correspondsto the measures of the base fact table aggregated to the proper level ofdetail.

The aggregation levels are determined from the hierarchical dimensions.Each dimension is structured as a tree with multiple levels. Each levelcorresponds to a different semantic level of detail for that dimension.Within each level of the tree there are many nodes. Each nodecorresponds to a value within the domain of the level of detail that thenode is in. The tree forms a set of parent-child relationships betweenthe domain values at each level of detail.

2.2 Data Exploration of Databases

Visualization is a powerful tool for exploring large data, both byitself and coupled with data mining algorithms. However, the task ofeffectively visualizing large databases imposes significant demands onthe human-computer interface to the visualization system. Theexploratory process is one of hypothesis, experiment, and discovery. Thepath of exploration is unpredictable, and analysts need to be able toeasily change both the data being displayed and its visualrepresentation. Furthermore, the analyst must be able to first reasonabout the data at a high level of abstraction, and then rapidly drilldown to explore data of interest at a greater level of detail. Thus, theinterface must expose the underlying hierarchical structure of the dataand support rapid refinement of the visualization.

One tool known in the art is Polaris. See, for example, Stolte, Tang,and Hanrahan, 2002, IEEE Transactions on Visualization and ComputerGraphics 8. Polaris is built upon an algebraic formalism forconstructing visualizations of relations. The state of the userinterface is a visual specification. This specification is interpretedaccording to the formalism to determine both the series of queriesnecessary to retrieve the requested data, as well as the mapping andlayout of the resulting tuples (rows of data in the database) intographical marks. However, the original form of Polaris does not make useof the structure of hierarchically structured dimensions that are foundin a hierarchical database. Therefore, the original form of Polaris doesnot provide a satisfactory means for the exploratory analysis ofdatabases having a hierarchical structure.

Other known art in the field can be broken down into two categories (i)the visual exploration of databases and (ii) the use of datavisualization in conjunction with data mining algorithms. These twocategories will be considered in turn.

2.2.1 Visual Data Exploration of Databases

Visual query tools such as VQE (Merthick et al., 1997, “An InteractiveVisualization Environment for Data Exploration,” Proc. of KnowledgeDiscovery in Databases, p. 2-9), Visage (Roth et al. 1996, “Visage: AUser Interface Environment for Exploring Information” in Proceedings ofInformation Visualization, p. 3-12), DEVise Livny et al. 1997, “DEVise:Integrated Querying and Visual Exploration of Large Datasets” in Proc.of ACM SIGMOD), and Tioga-2 (Woodruff et al. 2001, Journal of VisualLanguages and Computing, Special Issue on Visual Languages for End-userand Domain-Specific Programming 12, p. 551-571) have focused on buildingvisualization tools that directly support interactive databaseexploration through visual queries. Users can construct queries andvisualizations directly through their interactions with the interface.These systems have flexible mechanisms for mapping query results tographs and support mapping database tuples to retinal properties of themarks in the graphs. Of these systems, only Tioga-2 provides built-insupport for interactively navigating through and exploring data atdifferent levels of detail. However, the underlying hierarchicalstructure must be created by the analyst during the visualizationprocess. These visual query tools do not leverage the hierarchicalstructure that is already encoded in the database. Because of thisdrawback, VQE, Visage, DEVise, and Tioga-2 are not satisfactory toolsfor facilitating exploratory analysis of databases having a hierarchicalstructure.

Tools such as XmdvTool (Ward, 1994, “XmdvTool: Integrating multiplemethods for visualizing multi-variate data,” Proceedings of IEEEVisualization, pp. 326-336), Spotfire (BioNorth, Ottawa, Ontario,Canada, November, 2002) and Xgobi (Buja et ed., 1996, Journal ofComputational and Graphical Statistics 5, p. 78-99) provide the analystwith a set of predefined visualizations such as scatterplots andparallel coordinates. These systems are augmented with extensiveinteraction techniques (e.g., brushing and zooming) that can be used torefine the queries. However, such methods do not provide tools tointeractively construct and refine a wide range of displays to suit theanalysis process. Furthermore, of these systems, only XmdvTool supportsthe exploration of hierarchically structured data XmdvTool has beenaugmented with structure-based brushes (see Fual et al. Proc. ofInformation Visualization, October 1999, pp. 58-64) that allow the userto control the display's global level of detail (based on a hierarchicalclustering of the data) and to brush records based on their proximitywithin the hierarchical structure. However, such an approach limits theuser, in this case to viewing a single hierarchical structuring of thedata and a single ordering of that hierarchy to make proximitymeaningful. For this reason, XmdvTool, Spotfire, and Xgobi are notsatisfactory tools for facilitating exploratory analysis of databaseshaving a hierarchical structure.

Another known visualization system, VisDB (Keim and Kriegel, 1994, IEEEComputer Graphics and Applications 14, p. 40-49) focuses on displayingas many tuples (rows of data) as possible to provide feedback as usersrefine their queries. This system also displays tuples that do notsatisfy the query, indicating their “distance” from the query criteriausing spatial encodings and color. This approach helps the user avoidmissing important data points that fall just outside of the selectedquery parameters. However, VisDB fails to take advantage of thehierarchical structure of databases. For example, VisDB does not providean extensive ability to drill down and roll up data, thereby allowingthe analyst to get a complete overview of the data set before focusingon detailed portions of the database. For this reason VisDB is not asatisfactory tool for facilitating exploratory analysis of databaseshaving a hierarchical structure.

2.2.2 Visualization and Data Mining

Many research and commercial systems use visualization in conjunctionwith automated data mining algorithms. One common application ofvisualization together with data mining is in helping analystsunderstand models generated by the data mining process. For example,several researchers have developed techniques specifically fordisplaying decision trees, Bayesian classifiers, and decision tableclassifiers (Becker, 1998, Proc. of Information Visualization, p.102-105), and these visualization techniques have been incorporated intoproducts such as SGI's MineSet (Brunk et al., “MineSet: an integratedsystem for data mining,” Proceedings of the 3^(rd) InternationalConference on Knowledge Discovery and Data Mining, p. 135-138).

Other approaches to coupling visualization and data mining havetraditionally been employed within focused domains. One approach is touse visualization to gain an initial understanding of a database andthen apply algorithmic analysis to the identified areas of interest.See, for example, Kohavi, “Data Mining and Visualization,” Frontiers ofEngineering: Reports on Leading-Edge Engineering from the 2000 NAEsymposium on Frontiers of Engineering, National Academy Press, 2001 aswell as Therling et al. 2001, “Visualizing Data Mining Models,”Information Visualization in Data Mining and Knowledge Discovery,Fayyad, Frinstein, and Wierse; eds., Morgan Kaufman. The other majorapproach is to use data mining to compress the size and dimensionalityof the data and then use focused visualization tools to explore theresults. Se; for example, Healey, 1998, Proc. Graphics Interface, pp.177-184 as well as Welling and Derthick, 2000, “Visualization of LargeMulti-dimensional datasets,” Proceedings of Virtual Observatories of theFuture.

The drawback with the approaches described in this section is that theyare focused on a particular algorithm or a single phase of the discoveryprocess. For these reasons, known visualization and data mining tools donot provide a satisfactory way to explore and analyze databases thathave a hierarchical structure.

2.2.3 Table Based Displays

Another area of related work is visualization systems that usetable-based displays. Table displays such as scatterplot matrices(Hartigan, Journal of Statistical Computation and Simulation, 4, pp.187-213) and Trellis displays (Becker, Displays: A Multi-DimensionalData Visualization Tool for Data Mining, Third Annual Conference onKnowledge Discovery in Databases, August 1997) have been usedextensively in statistical data analysis. However, the drawback of suchvisualization systems is that they present static graphics that the usercannot interact with in order to refine database queries or otherwiseexplore database content.

Interactive table displays have also been developed. Pivot tables allowanalysts to explore different projections of large multi-dimensionaldatasets by interactively specifying assignments of fields to the tableaxes. However, pivot tables are limited to text-based displays.

The Table Lens (Rao and Card, The Table Lens: Merging Graphical andSymbolic Representations in an Interactive Focus+Context Visualizationfor Tabular In-formation, In Proc. of SIGCHI 1994, pp. 318-322) andFOCUS (Spenke et al. FOCUS: The Interactive Table for Product Comparisonand Selection. In Proc. of the ACM Symposium on User Interface Softwareand Technology, November 1996) visualization system provide tabledisplays that present data in a relational table view, using simplegraphics in the cells to communicate quantitative values. However, theTable Lens does not support queries. In addition, FOCUS is limited toobject-attribute tables that do not have hierarchical structure.

2.3 Formal Graphical Presentations

In addition to various software programs, the known art further providesformal graphical presentations. Bertin's Semiology of Graphics, 1983,University of Wisconsin Press, Madison Wis. is an early attempt atformalizing graphic techniques. Bertin developed a vocabulary fordescribing data and the techniques for encoding the data into a graphic.Berlin identified the retinal variables (position, color, size, etc.) inwhich data can be encoded. Cleveland (The Elements of Graphing Data,1985, Wadsworth Advanced Books and Software, Pacific Grove, Calif.;Visualizing Data, 1993, Hobart Press) used theoretical and experimentalresults to determine how well people can use these different retinalproperties to compare quantitative variations.

Mackinlay's APT system (ACM Trans. Graphics, pp. 110-141, April 1986) isone of the first applications of formal graphical specifications tocomputer generated displays. APT uses a set of graphical languages andcomposition rules to automatically generate two-dimensional displays ofrelational data. The Sage system (Roth et al., 1994, Proc. SIGCHI '94,pp. 112-117) extends the concepts of APT, providing a richer set of datacharacterizations and generating a wider range of displays.

Livny et al. (Proc. ACM SIGMOD, May 1997) describe a visualization modelthat provides a foundation for database-style processing of visualqueries. Within this model, the relational queries and graphical mappingnecessary to generate visualizations are defined by a set of relationaloperators. The Rivet visualization environment (Bosch et al., 2000,Computer Graphics, pp. 68-73) applies similar concepts to provide aflexible database visualization tool.

Wilkinson (The Grammar of Graphics, New York, Springer, 1999; U.S. Pat.No. 6,492,989) have developed a language for describing traditionalstatistical graphs. Further, Wilkinson proposes an interface forgenerating a subset of the specifications expressible within hislanguage.

The drawback with these known formal graphical specifications is thatthey do not provide any tools for generating a database query.Furthermore, Bertin's work is purely theoretical and was neverimplemented as a computer program. APT assumes a given databasestructure and automatically generates a graphic with no user involvementor support for user involvement. As such, these known formal graphicalspecifications do not provide a satisfactory way to analyze databases.

2.4 State of the Known Art

Programs used to visually explore databases have been described. Fromthis survey, it is apparent that known visualization and data miningtools do not provide a satisfactory way to explore and analyze databasesthat have a hierarchical structure. Thus, given the above background,what is needed in the art is an interactive visual exploration tool thatfacilitates exploratory analysis of databases having a hierarchicalstructure.

3. SUMMARY OF THE INVENTION

The present invention addresses the shortcomings oldie known art. Aninteractive visual exploration tool that facilitates exploratoryanalysis of databases is provided. The systems and methods of thepresent invention are not focused on a particular algorithm, a singlephase of the discovery process, or a narrow application domain. Rather,the systems and methods of the present invention can be used to gain aninitial understanding of a database, to visually explore the database,to understand relationships between the fields of the database, tounderstand algorithm output, or to interactively explore a mining model.The systems and methods of the present invention provide an ability toencode a large number of dimensions in a table layout in order to helpan analyst gain an initial understanding of how different dimensionsrelate as a precursor to automated discovery. Furthermore, the systemsand methods of the present invention can be used directly as a visualmining tool. By integrating the decision trees and classificationnetworks into the database as dimension hierarchies, the presentinvention can be used by analysts to gain an understanding of how thesemodels classify the data.

The present invention takes advantage of the hierarchical structure of adatabase in order to provide extensive ability to drill down and roll updata. In instances where the database does not have an explicitlydefined hierarchical structure, the present invention allows forconstruction of database hierarchy. In some cases, this is accomplishedwith user input. The methods of the present invention provide a novelformalism that allows an analyst to get a complete overview of the dataset before focusing on detailed portions of the database. The presentinvention further supports both the simultaneous exploration of multiplehierarchies (derived from semantic meaning or algorithmic analysis) andthe ability to reorder the hierarchy as needed.

One aspect of the invention provides a method for producing graphics. Inthe method a hierarchical structure of a first database is determined.Then, a visual table, comprising one or more panes, is constructed byproviding a specification that is in a language based on thehierarchical structure of the first database. The first database isqueried to retrieve a set of tuples in accordance with the specificationand a subset of this set of tuples is associated with a pane in the oneor more panes. In some embodiments the method further comprises encodinga tuple in the subset of the set of tuples in the pane as a graphicalmark.

In some embodiments, the specification organizes the one or more panesinto a plurality of rows and a plurality of columns that are optionallyorganized in a hierarchical manner. In some embodiments, specificationorganizes the one or more panes into a plurality of layers that areoptionally hierarchically organized. In some embodiments thespecification organizes the one or more panes into separate pages thatare hierarchically organized.

In some embodiments, the specification comprises an algebraic expressionthat includes an operand and the algebraic expression represents anoperation on the hierarchical structure of said first database. Thisoperand can be, for example, a type that appears in the hierarchicalstructure. The algebraic expression is evaluated thereby obtaining anordered set of tuples. The ordered set of tuples are then mapped to arow, a column, or a layer in the visual table. In such instances, therow, the column, or the layer is presented in the same order that is inthe ordered set of tuples. In some instances, the algebraic expressionincludes a relational operator such as cross product, union, selectionor sorting. In some instances, a precedence of the relational operatoris specified by a nesting operation (e.g., by the used of parentheses).

In some embodiments of the invention, the specification organizes theone or more panes into a plurality of rows and a plurality of columnsand the specification comprises a first algebraic expression for theplurality of rows and a second algebraic expression for the plurality ofcolumns. In such embodiments, at least one of the first algebraicexpression and the second algebraic expression represents an operationon the hierarchical structure of the first database. In someembodiments, the specification further organizes the one or more panesinto a plurality of layers and the specification further comprises athird algebraic expression for the plurality of layers.

In one aspect of the invention a hierarchical structure of a seconddatabase is determined. In this aspect of the invention, thespecification comprises an element of the hierarchical structure of thefirst database and an element of the hierarchical structure of thesecond database. Further, the specification includes an operand encodedas a type tuple that is derived from the first database or the seconddatabase.

In another aspect of the invention, a tuple in the subset of tuplesassociated with a pane in the visual table comprises a field. In someinstances the field is mapped to a graphical attribute such as a color,a value, a size, a shape, a phrase, or a symbol. In some instances thefield is classified as quantitative or ordinal. The field is mapped to afirst graphical attribute when the field is classified as quantitative.The field is mapped to a second graphical attribute when the field isclassified as ordinal. Instill other instances, the field is classifiedas independent or dependent. The field is mapped to a first graphicalattribute when the field is classified as independent. The field ismapped to a second graphical attribute when the field is classified asdependent. Here, the first graphical attribute and the second graphicalattribute are each independently a color, a value, a size, a shape, aphrase, or a symbol.

In still another aspect of the invention, a group is created with all ora portion of the tuples in the set of tuples and a graphic is formedbased on the group. This graphic can be, for example, a line thatconnects each tuple in the group. In another example, the graphic is anarea that encloses each tuple in the group. In some embodimentsmultiple-tuple marks (e.g., for lines and polygons) are created in step616. In such instances a single mark that is based off multiple tuplesis created. For example, a polygon representing a U.S. state can betreated as a single mark.

In yet another aspect of the invention, the first database is queriedwith a query that is based upon the specification. In some embodimentsin accordance with this aspect of the invention, the querying furthercomprises mapping the query to a relational algebra operator such as astructured query language (SQL) query or a datacube query (e.g., an MDXquery). In some instances, the specification is processed therebyreducing a number of queries that are performed by the querying processof the invention. In one example, the query is processed by crossing anexpression in the specification, converting an expression in thespecification to a sum-of-terms, and forming a query from a term in thesum-of-terms.

In one aspect of the invention the hierarchical structure of eachdatabase in a plurality of databases is determined. Further, thespecification is written in a language based on a hierarchical structureof one or more databases in the plurality of databases. In this aspectof the invention, the querying comprises accessing all or a portion ofthe databases in the plurality of databases.

In another aspect in accordance with the invention, a hierarchicalstructure of each database in a plurality of database is determined.Further, the language is based on a hierarchical structure of all or aportion of the databases in the plurality of database. All or a portionof the plurality of databases is queried. Further, the set of tuplesincludes tuples derived from all or a portion of the plurality ofdatabases. In one embodiment in accordance with this aspect of theinvention, the specification organizes the one or more panes into aplurality of layers and each layer in the plurality of layers isassigned to a tuple from a different database in the plurality ofdatabases. In another embodiment in accordance with this aspect of theinvention, the specification organizes the one or more panes into aplurality of columns and a plurality of rows and each column in theplurality of columns is assigned to a tuple from a different database insaid plurality of databases. In still another embodiment in accordancewith this aspect of the invention, the specification organizes the oneor more panes into a plurality of columns and a plurality of rows andeach row in the plurality of rows is assigned to a tuple from adifferent database in the plurality of databases. In yet anotherembodiment in accordance with this aspect of the invention, thespecification organizes the one or more panes into a plurality of pagesand each page in the plurality of pages is assigned to a tuple from adifferent database in the plurality of databases.

In yet another aspect of the invention, the hierarchical structure ofthe first database includes a plurality of schema fields. In this aspectof the invention, construction of the visual table further comprisesassigning a schema field in the plurality of schema fields to a pane inthe one or more panes based on the specification. Further, the subset ofthe set of tuples associated with the pane is determined by a selectionfunction. In some embodiments, the selection function uses an identityof the schema field. In some embodiments, the selection function uses arelational operator (e.g., a selection operator or a grouping operator)to form the subset. In some embodiments, the selection function uses arelational operator (e.g., a sorting operator, an aggregation operator,a transforming operator, etc.) to create a new tuple from the subset oftuples that is associated with the pane.

In another aspect of the invention, the constructing a visual table, thequerying of the database, and the associating of a subset of tuples to apane is repeated using a specification that is determined by the subsetof the set of tuples associated with the pane. In some instances thesesteps are repeated using one or more tuples in the subset of tuples thatare selected by a user.

In still another aspect of the invention, the first database has aschema and the language comprises a plurality of fields in this schema.Further, the visual table comprises a plurality of axes and each axis inthe plurality of axes is represented by a shelf. The specificationcomprises one or more algebraic expressions. One such algebraicexpression is created by dragging a field in the plurality of fields inthe database schema onto a shelve that represents an axis of the visualtable thereby constructing an algebraic expression in the specification.

In still another aspect of the invention, the specification is stored(e.g., as a bookmark, an undo operation, a redo operation, etc.). Insome embodiments, the first database is a flat file, a relationaldatabase, or an on-line analytical processing database. In someembodiments, the first database is a hierarchical on-line analyticalprocessing data cube. In some embodiments, the first database does nothave an explicitly defined hierarchy. When this is the case, data fieldsin the first database are analyzed to determine hierarchical structurewithin the database. In some embodiments, the first database has a starschema that is analyzed to determine the hierarchical structure of thedatabase. In some embodiments, the first database is hosted by remotecomputer.

Yet another aspect of the invention provides a computer program productfor use in conjunction with a computer system. The computer programproduct comprises a computer readable storage medium and a computerprogram mechanism embedded therein. The computer program mechanismcomprises (i) a first database, (ii) a database hierarchy modulecomprising instructions for determining a hierarchical structure of thefirst database, (iii) a user interface module comprising instructionsfor constructing a visual table, comprised of one or more panes, byobtaining from a user a specification that is in a language based on thehierarchical structure of the first database, (iv) a data interpretermodule comprising instructions for querying the first database toretrieve a set of tuples in accordance with the specification, and (v) avisual interpreter module comprising instructions for associating asubset of the set of tuples with a pane in the one or more panes.

Still another aspect of the invention provides a computer system forproducing graphics. The computer system comprise a central processingunit and a memory coupled to the central processing unit. The memorystores (i) a first database, (ii) a database hierarchy module comprisinginstructions for determining a hierarchical structure of the firstdatabase, (iii) a user interface module comprising instructions forconstructing a visual table, comprised of one or more panes, byobtaining from a user a specification that is in a language based on thehierarchical structure of the first database, (iv) a data interpretermodule comprising instructions for querying the first database toretrieve a set of tuples in accordance with the specification, (v) and avisual interpreter module comprising instructions for associating asubset of the set of tuples with a pane in the one or more panes.

4. BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates raw data in the form of product managers' quarterlysales reports in accordance with the prior art.

FIG. 2 illustrates raw data in the form of regional managers' quarterlysales reports in accordance with the prior art.

FIG. 3 illustrates a star schema for a database in accordance with theprior art.

FIG. 4 illustrates a snowflake schema for a database in accordance withthe prior art.

FIG. 5 illustrates a computer system that facilitates exploratoryanalysis of databases having a hierarchical structure in accordance withone embodiment of the present invention.

FIG. 6 illustrates processing steps in accordance with one embodiment ofthe present invention.

FIG. 7 illustrates a user interface for creating a visual specificationin accordance with one embodiment of the present invention.

FIG. 8 illustrates examples of the set interpretations and resultingtable structures for representative expressions in accordance with oneembodiment of the present invention.

FIG. 9 illustrates a hierarchy for time.

FIG. 10 illustrates a table in accordance with the present invention.

FIG. 11 provides an exemplary view of processing steps in accordancewith one embodiment of the present invention.

FIG. 12 illustrates the configuration for a table that has beengenerated from the normalized set form of a visual specification inaccordance with one embodiment of the present invention.

FIG. 13 illustrates a data cube for a hypothetical coffee chain in whicheach axis in the data cube corresponds to a level of detail for adimension (product, location, time) in a database schema, in accordancewith the prior art.

FIG. 14 illustrates a lattice of data cubes for a particular databaseschema in which each dimension has a hierarchical structure, inaccordance with the prior art.

FIG. 15 illustrates the projection of a 3-dimensional data cube therebyreducing the dimensionality of the data cube by aggregating acrossdimensions that are not of interest to an analysis, in accordance withthe prior art.

FIG. 16 illustrates the construction of a slice of a data cube byfiltering the members of one or more dimensions of the cube, inaccordance with the prior art.

FIG. 17 illustrates the layering of multiple data sources and thepartitioning of layers in accordance with one embodiment of the presentinvention.

FIG. 18 illustrates the association of a subset of tuples with a pane inone or more panes in a visual table in accordance with one embodiment ofthe present invention.

FIGS. 19A-19C illustrate an analysis of network usage in accordance withan embodiment of the present invention.

FIGS. 20A-20C illustrate an analysis of the results of the 2000presidential election in accordance with an embodiment of the presentinvention.

FIGS. 21A-21C illustrate an analysis of sales data for a hypotheticalcoffee chain in accordance with an embodiment of the present invention.

5. DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a method for exploiting the hierarchicalinformation present in databases in order to facilitate exploration ofsuch databases. The present invention uses a novel formulism toaccomplish this task. A user is allowed to enter a search query that isconsistent with the novel formalism of the present invention. When sucha search query is constructed, the systems and methods of the presentinvention take advantage of the formalism and the hierarchicalinformation associated with the target database to service the queryusing fewer existence scans and other time consuming database functionsthan are found in known data exploration programs and techniques.Additional features and advantages of the present invention aredisclosed in the following sections.

5.1 Overview of an Exemplary System

FIG. 5 shows a system 500 that facilitates exploratory analysis ofdatabases, such as data warehouses, in accordance with one embodiment ofthe present invention.

System 500 preferably comprises a computer 502 that includes:

-   -   a central processing unit 522;    -   a main non-volatile storage unit 534, preferably including one        or more hard disk drives, for storing software and data, the        storage unit 534 typically controlled by disk controller 532;    -   a system memory 538, preferably high speed random-access memory        (RAM), for storing system control programs, data, and programs,        including programs and data loaded from non-volatile storage        unit 534; system memory 538 may also include read-only memory        (ROM);    -   a user interface 524, including one or more input devices, such        as a mouse 526, a keypad 530, and a display 528;    -   an optional network interface card 536 for connecting to any        wired or wireless communication network; and    -   an internal bus 533 for interconnecting the aforementioned        elements of the system.

Operation of computer 502 is controlled primarily by operating system540, which is executed by central processing unit 522. Operating system540 can be stored in system memory 538. In addition to operating system540, a typical implementation of system memory 538 includes:

-   -   file system 542 for controlling access to the various files and        data structures used by the present invention;    -   database hierarchy module 544 for interpreting the hierarchy of        a database 558 (e.g., by interpreting the database schema);    -   user interface module 546 for obtaining a visual specification        (specification) from the user (for constructing a visual table,        comprised of one or more panes, by obtaining from a user a        specification that is in a language based on the hierarchical        structure of database 558);    -   data interpreter module 552 for formulating database queries        based on the specification (for querying database 558 to        retrieve a set of tuples in accordance with the specification);        and    -   visual interpreter module 556 for processing database query        results and displaying these results in accordance with the        specification (for associating a subset of the set of tuples        with a pane in the one or more panes).

In a preferred embodiment, user interface module 546 includes:

-   -   a database hierarchy 548 that corresponds to the hierarchy of a        database 558; and    -   a visual specification 550 that specifies a formalism that can        be used to determine the exact analysis, query, and drawing        operations to be performed by the system.

In a preferred embodiment, data interpreter module 552 includes:

-   -   one or more query descriptions 554 that are used to query        databases;    -   a query cache 555 that is used to store database query results;        and    -   a pane-data-cache 557 that is used to store a separate data        structure for each pane 722 (FIG. 7) in a visual table 720 that        is displayed by visual interpreter module 556.

System 500 includes one or more databases 558. In one embodiment adatabase 558 is OLAP data that can be viewed conceptually as amultidimensional data cube. See, for example, Section 5.3. Moregenerally, database 558 is any form of data storage system, includingbut not limited to a flat file, a relational database (SQL), and an OLAPdatabase (MDX and/or variants thereof). In some specific embodiments,database 558 is a hierarchical OLAP cube. In some specific embodiments,database 558 comprises star schema that is not stored as a cube but hasdimension tables that define hierarchy. Still further, in someembodiments, database 558 has hierarchy that is not explicitly brokenout in the underlying database or database schema (e.g., dimensiontables are not hierarchically arranged). In such embodiments, thehierarchical information for the respective database 558 can be derived.For example, in some instances, database hierarchy module 544 readsdatabase 558 and creates a hierarchy representing data stored in thedatabase. In some embodiments, this external program is run with userinput. In some embodiments, there is only a single database 558.

In typical embodiments, one or more of databases 558 are not hosted bycomputer 502. Rather, in typical embodiments, databases 558 are accessedby computer 502 using network interface 536. In some embodiments anattribute file 580 is associated with each database 558. Attributes arediscussed in Section 5.3.6, below.

It will be appreciated that many of the modules illustrated in FIG. 5can be located on a remote computer. For example, some embodiments ofthe present application are web service-type implementations. In suchembodiments, user interface module 546 can reside on a client computerthat is in communication with computer 502 via a network (not shown). Insome embodiments, user interface module 546 can be an interactive webpage that is served by computer 502 to the client computer. Further,some or all of the components of visual interpreter module 556 canreside on the client computer so that the results of a query aredisplayed on the client computer. Thus, the present invention fullyencompasses a broad array of implementations in which one or more userscan explore one or more databases 558 using the techniques and methodsof the present invention from a remote site. The illustration of themodules in FIG. 5 in a single central computer is merely presented toconcisely illustrate certain software modules and data structures thatare used in various embodiments of the present invention and in no wayis limiting. Those of skill in the art will appreciate that numerousother configurations are possible and all such configurations are withinthe scope of the present invention.

Now that an overview of a system 500 in accordance with one embodimentof the present invention has been described, various advantageousmethods in accordance with the present invention will now be disclosedin the following sections.

5.2 Exemplary Method

Referring to FIG. 6, an exemplary method in accordance with oneembodiment of the present invention is illustrated.

Step 602. In step 602, the hierarchy for each selected database 558 ischaracterized. In embodiments in which selected databases 558 have aschema 560 that includes such hierarchical information; the schema 560can be mad directly by database hierarchy module 544 and the databasehierarchy 562 in this schema 560 can be characterized. Section 5.3discusses illustrative types of database hierarchy 562 and databaseorganization. In some embodiments, a plurality of databases 558 isanalyzed concurrently. In such embodiments, database schema 560 of eachof the plurality of databases 558 is read directly by database module544 and characterized. In some embodiments, selected databases 558 donot have hierarchy that is explicitly defined in the underlyingrespective databases 558. In such embodiments, database hierarchy module544 analyses each selected database 558 and constructs databasehierarchical information for each of the respective databases. In someinstances, this analysis is assisted by input from a user and/orrequires an analysis of the data stored in the database.

In some embodiments, the hierarchical structure of a database 558 isderived form a database schema for the database 558. This databaseschema comprises schema fields. In some embodiments each schema fieldhas a type (e.g., a base type or an array type). Representative basetypes include, but are not limited to, character strings, integer, shortinteger, double integer, single precision floating number, doubleprecision floating point number, and object handle. Representative arraytypes include, but are not limited to an array of long integers, anarray of short integers, an array of single precision floating pointnumbers, an array of double precision floating point numbers and anarray of object handles.

Step 604. In step 604, a visual specification (specification) 550 isobtained from the user by user interface module 546. In a preferredembodiment, visual specification 550 is created using a drag-and-dropinterface provided by user interface module 546. An exemplary userinterface module 546 is illustrated in FIG. 7. A user creates the visualspecification by dragging operand names from schema box 702 to variousshelves 708 throughout the interface. These operand names are derivedfrom the hierarchical structure of each selected database 558 that wascharacterized in step 602. For example, one of the dimensions availablefor exploration in the database could be “time.” Then, likely operandnames available in schema box 702 would be “year”, “quarter”, “month”,and “day”. Each of these operand names is referred to as a type tuple.In some embodiments, more than one database 602 is characterized in step602. Further, specification 550 can comprise a first element of thehierarchical structure of a first database 558 characterized in step 602and a second element of the hierarchical structure of the seconddatabase characterized in step 602. The first element comprises a typetuple that is derived from the first database 558 and the second elementcomprises a type tuple that is derived from the second database 558.

Schema box 702 of FIG. 7 includes a representation of the databaseschema for each of the one or more databases 558 being analyzed. Schemabox 702 includes each dimension 704 represented in each schema 560 ofeach database 558 that is being analyzed. For example, in FIG. 7, asingle database that includes the dimensions “time” 704-1, “products”704-2, and “location” 704-3 is analyzed. An ordered list of thedimension's levels is placed below each dimension. For example, in thecase of time 704-1, the ordered list includes the dimension levels“year”, “quarter”, and “month”. In the case of products, the orderedlist includes the dimension levels “producttype” and “product”. In thecase of location, the ordered list includes the dimension levels“market” and “state”.

A user can drop any dimension level into the interface of shelves 708.However, the dimensions 704 cannot be dragged into the shelves. Shelves708-4 and 708-5 are the axis shelves. The operands placed on shelves708-4 and 708-5 (e.g., year, quarter, month, producttype, product,market, state) determine the structure of visual table 720 and the typesof graphs that are placed in each pane 722 of visual table 720. Forexample, in FIG. 7, the value “sales”, which belongs to the dimension“Producttype” has been placed on shelf 708-4. Therefore, the y-axis ofvisual table 720 is a breakdown of the sales of each “producttype”.Valid product types include “coffee”, “espresso”, “herbal tea”, and“tea.” Thus, the y-axis of visual table 720 represents the sale of eachof these products. In FIG. 7, the value “profit”, which belongs to theoperand “Quarter” (which is part of the dimension “time”) has beenplaced on shelf 708-5. Thus, the x-axis of visual table 720 representsprofit. Level of detail shelf 708-2 has been set to state. Accordingly,each mark in each pane 722 in visual table 720 represents data for aparticular state.

The configuration of operands on shelves 708 (FIG. 7) forms the visualspecification 550 (FIG. 5). At a minimum, a visual specification 550includes an x-axis expression and a y-axis expression. More typically, avisual specification 550 further includes a z-axis expression, which isplaced on shelf 708-1, and a level of detail expression 708-2. Arepresentative visual specification 550 is provided in FIG. 11 (element550). The visual specification includes the following expressions:x:C*(A+B)y:D+Ez:Fand the level of detail within each pane 722 is set to:level of detail: G

In some embodiments, a user can specify any of the algebra (e.g.,ordinal concatenation, etc.) described in Section 5.4. In someembodiments, a user types in the algebra directly using a user interfacesuch as the one illustrated in FIG. 7, includes it in a file that isthen interpreted, or uses some other form of data entry known in theart.

In some embodiments, the each shelve 708 that represents an axis ofvisual table 720 is translated into corresponding expressions in anautomated manner. For example the contents of the shelf 708 thatrepresents the x-axis is translated into an expression that representsthe x-axis of visual table 720, the shelf 708 that represents the y-axisis translated into an expression that represents the y-axis of visualtable 720, and the shelf 708 that represents layers is translated intoan expression that represents the z-axis of visual table 720. Thecontents of each axis shelve 708 is an order list of database fieldnames. In some embodiments, the order of the database field names isconstrained such that all nominal and ordinal fields precede allquantitative fields in the shelf. Exemplary nominal fields include, butare not limited to products, regions, account numbers or people.Exemplary ordinal fields include, but are not limited to dates orpriority rankings. Exemplary quantitative fields include, but are notlimited to profit, sales, account balances, speed or frequency. Inembodiments where the order of the database field names is constrainedsuch that all nominal and ordinal fields precede all quantitative fieldsin the shelf 708, the nominal fields are assigned an ordering andtreated as ordinal. This ordering is either a natural ordering (e.g.,alphabetic, numeric) or an ordering specified by the user. Then, thelist of fields in respective shelf are transformed into an expression ofthe form(O ₁ ×O ₂ . . . ×O _(n))×(Q ₁ ×Q ₂ . . . ×Q _(m))In addition, if any two adjacent categorical fields represent levels ofthe same dimension then the cross “x” operator (see Section 5.4.22)between them is replaced with a dot “.” operator (see Section 5.4.2.4).The specification is used to map data values from a database 558 tovisual properties by visual interpreter module 556. Further shelveslabeled “Group in panes by” (not shown) and “Sort in panes by” (708-3,FIG. 7) define the “Group” and “Sort Order” components of the visualspecification.

In some embodiments, the specification is written in a language that isbased on the metadata (e.g., hierarchical structure) of the one or moredatabases 558 that were characterized in step 602. At a minimum, thislanguage comprises all or a portion of the dimension levels that make upthe hierarchies of the one or more databases 558. Examples of dimensionlevels (e.g., year, quarter, month, etc.) have been described.Typically, these dimensional levels are displayed on user interface 524as illustrated in FIG. 7. In some embodiments, the language furtherincludes a table algebra, such as the algebra described in Section 5.4below, that allows the user to form complex visual tables comprised ofone or more panes 722 (FIG. 7). In embodiments where the specification550 makes use of the table algebra in the form of an algebraicexpression, the specification includes at least one operand. An operandis a dimension level or a measure/quantitative variable from thedatabase schema (or other database metadata) that has been selected forinclusion in the algebraic expression. In addition to the at least oneoperand, the algebraic expression includes one or more operators thatrepresent operations on the metadata of the one or more databases 558that were characterized in step 602. Examples of such operators include,but are not limited to, relational operators such as cross product(Section 5.4.2.2), union, selection or sorting. Other examples ofoperations include, but are not limited to, the nest operator (Section5.42.3) and the dot operator (Section 5.4.2.4). The nest operatoranalyzes a fact table within a database whereas the dot operatoranalyses a dimension table (or equivalent data structure) associatedwith a database 558 that defines the database 558 hierarchy. Analysis ofthe fact table by the nest operator (Section 5.4.2.3) or the dimensionaltable (or equivalent data structure) by the dot operator (Section5.42.4) represents an operation on the hierarchical structure of theassociated database 558. The operations and operators within thealgebraic expressions can be nested. For example, in one embodiment,parentheses are used to alter the order in which operators areconsidered.

In a preferred aspect of the present invention, visual specification 550organizes panes 722 into a plurality of rows and a plurality of columns.In embodiments in accordance with this aspect of the invention, visualspecification 550 includes a first algebraic expression for theplurality of rows and a second algebraic expression for the plurality ofcolumns. Both the first algebraic expression and the second algebraicexpression each represent an operation on the metadata of a database 558(e.g., hierarchical structure) that was characterized in step 602. Insome instances in accordance with this aspect of the invention, thespecification further organizes one or more panes 722 into a pluralityof layers. To accomplish this, the specification 550 further comprises athird algebraic expression for the plurality of layers. The thirdalgebraic expression represents an operation on the metadata of one ormore of the databases 558 that were characterized in step 602. Forexample, the first two algebraic expressions could cover revenue for allproducts whereas the third algebraic expression could add the dimension“State” such that each layer represents the revenue by product for eachstate.

Using the methods of the present invention, each visual specification550 can be interpreted to determine the exact analysis, query, anddrawing operations to be performed by system 500. In a preferredembodiment, drawing operations are performed independently in each pane722 of visual table 720.

Visual table 720 includes three axes. The x and y axes are respectivelydetermined by shelves 708-5 and 708-4, as discussed above. The z axis isdetermined by shelf 708-1 (FIG. 7). Each intersection of the x, y, andz-axis results in a table pane 722. Each pane 722 contains a set ofrecords, obtained by querying a database 558, that are visually encodedas a set of marks to create a visual table. While shelves 708-1, 708-4,and 708-5 determine the outer layout of visual table 720, other shelves708 in display 700 determine the layout within a pane 722. In someembodiments, this inner layout includes the sorting and filtering ofoperands, the mapping of specific databases 558 to specific layers inthe z axis of visual table 720, the grouping of data within a pane 722and the computation of statistical properties and derived fields, thetype of graphic displayed in each pane 722 (e.g., circles, bars, glyphs,etc.), and the mapping of data fields to retinal properties of the marksin the visual tables (e.g., mapping “profit” to the size of the mark andmapping “quarter” to color).

Step 606. In step 606, a set of efficient queries is formed by datainterpreter module 552 based on specification 550. Before generatingdatabase specific queries, data interpreter module 552 generates a setof one or more abstract query descriptions 554 that describe therequired queries using the values specified in visual specification 550(e.g., values placed on shelves 708-1; 708-4, and 708-5). Querydescriptions 554 precisely describe the desired filtering, sorting, andgrouping of tuples from database 558.

The number of distinct query descriptions 554 that are generated for asingle visual specification 550 is determined by the level of detailspecified in visual specification 550. For example, visual table 720(FIG. 10) shows a simple-text based visual table 720 that requires twoseparate queries to generate because of the use of the concatenationoperator in the y-axis expression. In some embodiments, the level ofdetail within a pane 722 in a visual table 720 is determined by both thelevel of detail shelf 708-2 and the table algebra expressions formed inshelves 708-1, 708-4, and 708-5 (FIG. 7).

Although it is possible for each pane 722 to correspond to a differentlevel of detail, and thus a different query, the common situation is fora larger number of panes 722 (FIG. 7) to correspond to the same level ofdetail and differ only by how the tuples are filtered. For efficiency,it is preferred to considered panes 722 that require the same level ofdetail as a group and send a single query to a database 558 requestingthe appropriate tuples. The tuples can then be partitioned into panes722 locally in subsequent processing steps. Accordingly, in one aspectof the invention, database queries are grouped. In some embodiments,this is accomplished by algebraically manipulating visual specification550 in order to determine the queries that are required for a givenvisual table 720. Of all the algebraic operators used in the algebra ofthe present invention (see, for example, Section 5.4, below), theoperator that can produce adjacent panes 722 with differing projectionsor level of detail is the concatenate operator. Nest, cross, and dot,described in more detail in Section 5.4, below, include all inputdimension levels in each output p-tuple. Concatenate does not. Thus, ifeach axis expression in the visual specification 550 is reduced to asum-of-terms form, the resulting terms will correspond to the set ofqueries that need to be retrieved from one or more databases 558.

To illustrate the sum-of-terms reduction of each axis, considerexemplary visual specification 550 in FIG. 11, in which:x:C*(A+B)y:D+Ez:Fand the level of detail within each pane 722 is set to G. Crossing theseexpressions, in accordance with the table algebra specified in Section5.4, below, and then reducing to a stun-of-terms form yields:(A*C*D*F*G)+(A*C*E*F*G)+(B*C*D*F*G)+(B*C*E*G)Thus, in this example, the following four database queries are made:(A*C*D*F*G)  Query 1(A*C*E*F*G)  Query 2(B*C*D*F*G)  Query 3(B*C*E*G)  Query 4

Most typical multidimensional query languages provide a mechanism forgenerating queries of the form found in queries 1-4. For example, eachof queries 1-4 can be a single multidimensional expressions (MDX) query.MDX (Microsoft, Redmond Wash.), is a syntax that supports the definitionand manipulation of multidimensional objects and data. MDX is similar tothe structured query language (SQL) syntax, but is not an extension ofthe SQL language. As with an SQL query, each MDX query requires a datarequest (SELECT clause), a starting point (FROM clause), and a filter(WHERE clause). These and other keywords provide the tools used toextract specific portions of data from a hierarchical database (e.g., acube) for analysis. In summary, each query can map to a relationalalgebra operator such as an SQP query or to a datacube query (e.g., anMDX query).

Now that an overview of how visual specification 550 is reduced to anefficient set of queries has been presented, a detailed algorithm usedin one embodiment of the present invention will be described. Thealgorithm is set forth in the following pseudo code:

101: x-terms = List of terms from the sum-of-terms form of the x-axisexpression 102: y-terms = List of terms from the sum-of-terms form ofthe y-axis expression 103: z-terms = List of terms from the sum-of-termsform of the z-axis expression 104: for each layer { 105: for each x-termin x-terms { 106: for each y-term in y-terms { 107: for each z-term inz-terms { 108: p-lookup = PaneLookupDescriptor(x-term, y-term, z-term)109: p-spec = The PaneSpecification that applies to p-lookup 110: qd =newQueryDescription 111: Add to qd all fields in x-term 112: Add to qdall fields in y-term 113: Add to qd all fields in z-term 114: Add to qdall level of detail fields in p-spec 115: Add to qd all drawing orderfields in p-spec 116: Add to qd all encoding fields in p-spec 117: Addto qd all selection (brushing / tooltips) fields in p-spec 118: Add toqd all filters in the visual specification involving the fields in qd119: if (qd matches date in data-cache) 120: results = retrieve datafrom data-cache 121:  else 122: results = retrieve data from databaseserver 123:  add results to data-cache indexed by qd 124:  group-tsf =create GroupingTransform 125:  run group-tsf 126:  Add each output datastructure from group-tsf to  pane-data-cache }}}}

Lines 101 through 103 of the pseudo code represent the case in whicheach axis of visual specification 550 is reduced to the sum-of-terms.Then, lines 104 through 107 are used to individually consider each ofthe terms i. Individually, each term i describes either a set of rows, aset of columns, or a set of layers in visual table 720. Together, theterms define a set of panes 722 that are all at the same level of detail708-6 (FIG. 7). Thus, lines 104 through 107 can be read as “for eachx-term, y-term, z-term combination”.

Lines 108 and 109 are used to find the pane specification, which definesthe marks, encodings, etc., for the panes 722 defined by a particularx-term, y-term, z-term combination. This is done by testing p-lookupagainst the selection criteria predicate in each pane specification inthe visual specification.

Lines 110 through 118 build a query for the particular x-term, y-term,z-term combination. Line 110 creates the variable “qd” to hold the queryand lines 111 through 113 adds all the fields in the x-term, the y-term,and the z-term in the particular x-term, y-term, z-term combination.Lines 114 through 118 add additional terms from visual specification550, such as level of detail, to the query.

Next, in lines 119 through 122, a determination is made as to whether aquery of the form built by lines 110 through 118 already exists in thedata-cache (query cache 555, FIG. 5). If so, the result is retrieve fromthe data cache (line 120, from query cache 555, FIG. 5). If not, theserver that hosts the target database 558 is queried (line 122) usingthe query built by lines 110 through 118. If such a database query ismade, data interpreter module 552 will formulate the query in adatabase-specific manner. For example, in certain instances, datainterpreter module 552 will formulate an SQL query whereas in otherinstances, data interpreter module 552 will formulate an MDX query. Inline 123, the results of the query is added to the data-cache (to querycache 555, FIG. 5).

The data retrieved in the processing steps above can contain data for aset of panes 722. When this is the case, the data is partitioned into aseparate data structure for each pane 722 using a grouping transform(lines 124-125) that is conceptually the same as a “GROUP BY” in SQLexcept separate data structures are created for each group rather thanperforming aggregation. In line 126, each output data structure fromgroup-tsf is added to pane-data-cache 557 (FIG. 5) for later use byvisual interpreter module 556.

Step 608. In step 608, the queries developed in step 608 are used toquery one or more databases 558. Such databases 558 can be stored inmemory 548. However, in a more preferred embodiment, these databases 558are stored in a remote server.

Step 610. In step 610, visual interpreter module 556 processes queriesthat have been generated by data interpreter module 552. A number ofsteps are performed in order to process these queries. An overview ofthese steps is illustrated in FIG. 11. In step 612, visual specification550 is reduced to a normalized set form 1104. In step 614, visual table720 is constructed using the normalized set form. In step 616, the queryresults are partitioned into tuples corresponding to the panes 722 invisual table 720. Bach of these steps will now be described in furtherdetail so that the advantages of the present invention can beappreciated.

Step 612—reduction of the visual specification to the normalized setform. In step 604, visual specification 550 was obtained by userinterface module 546. The visual specification 550 comprises the valuesof shelves 708 that have been populated by the user. In step 612, visualspecification 550 is used to construct algebraic expressions that definehow visual table 720 is partitioned into rows, columns, and layers, andadditionally defines the spatial encodings within each pane 722 ofvisual table 720. In this way, visual specification 550 organizes one ormore panes 722 into a plurality of rows and a plurality of columns. Insome embodiments, the plurality of rows and plurality of columns ishierarchically organized. Further, in some embodiments specification 550also organizes the one or more panes 722 into a plurality of layers thatare optionally hierarchically organized. Further still, in someembodiments, the specification organizes the one or more panes 722 intoseparate pages that are optionally hierarchically organized.

A complete algebraic expression of visual table 720 is termed a “tableconfiguration.” In other words, in step 612, the three separateexpressions of visual specification 550 that respectively define the x,y, and z axes of visual table 720 are normalized to set form (setinterpreted) in order to partition the row, columns and layers of visualtable 720. To produce the normalized set form, each operand in the threeseparate expressions is evaluated to set form. The operators in eachexpression define how to evaluate each set within an expression. Thus,normalization to set form results in a single set (the normalized setform), where each element in the normalized set form corresponds to asingle row, column, or layer of visual table 720. In some embodiments,this normalization process is extended to yet another dimension, terms“pages”.

Recall that each expression in the three separate expressions of visualspecification 550 that define the x, y, and z axis are drawn fromoperands (e.g., fields) in the database schema. The algebra used toproduce the normalized set form characterizes each of the operands in adatabase schema (or some other representation of database structure)into two types: dimension levels and measure. Whether an operand is adimensional level or a measure depends on the type of the operand. Theset interpretation of an operand consists of the members of the orderdomain of the operand. The set interpretation of the measure operand isa single-element set containing the operand name. For example, the setinterpretation of the “Profit” operand is {Profit}.

The assignment of sets to the different types of operands reflects thedifference in how the two types of operands are encoded into thestructure of visual table 720. Dimensional level operands partition thetable into rows and columns, whereas measure operands are spatiallyencoded as axes within table panes. Examples of the set interpretationsand resulting table structures for representative expressions isillustrated in FIG. 8.

A valid expression in the algebra used in the present invention is anordered sequence of one or more operands with operators between eachpair of adjacent operands. The operators in this algebra, in order ofprecedence are cross (x), nest (/), and concatenation (+). Parenthesescan be used to alter the precedence. Because each operand is interpretedas an ordered set, the precise semantics of each operator is defined interms of how they combine two sets (one each from the left and rightoperands) into a single set, as illustrated in FIG. 8.

Thus, every expression in visual specification 550 can be reduced to asingle set, with each entry in the set being an ordered concatenation ofzero or more dimension level values followed by zero or more measureoperand names. For example, the normalized set form of the expression“month×profit” is {(Jan, Profit), (Feb, Profit), . . . , (Dec, Profit)}.The normalized set form of an expression determines one axis of visualtable 720. The table axis is partitioned into columns (or rows orlayers) so that there is a one-to-one correspondence between columns andentries in the normalized set.

Now that an overview of step 612 has been described, an example will begiven. Consider the exemplary visual specification 550 of FIG. 11:x:C*(A+B)y:D+Ez:FComputation of the normalized set form of this visual specification, inaccordance with step 612 provides:x:{(c ₁ ,a ₁) . . . (c _(k) ,b _(j))}y:{(d ₁), . . . ,(d ₁),(e ₁), . . . ,(e _(m))}z:{(f ₁), . . . (f _(n))}

Advantageously, the algebraic formalisms of the present invention canmake use of an operator, termed the dot operator, that is specificallydesigned to work with dimension levels. Thus, the algebraic formalismsprovide direct support for the use and exploration of database hierarchyin the present invention. One of the advantages of the dot operator isthat it can deduce hierarchical information without analyzing databasefact tables. Further advantages of the dot operator are discussed inSection 5.42.4, below.

Step 614—construction of visual table 720 using the normalized set form.In step 614 (FIG. 6, FIG. 11), visual interpreter 556 constructs visualtable 720 using the normalized set form of the expressions for the x, y,and z-axis obtained from visual specification 550. Each element in thenormalized set form of the expressions for the x, y, and z-axiscorresponds to a single row, column or layer.

FIG. 12 illustrates the configuration for a visual table 720 that hasbeen generated from the normalized set form of a visual specification.FIG. 12 displays Profit information for the coffee chain data set(COFFEE). The y-axis is defined by the expressionProfit+(Market×ProductType) and the x-axis is defined by the expression(Quarter/Month). The z-axis is not illustrated in FIG. 12.

As illustrated in FIG. 12, expressions 1202 and 1204 are composed ofoperands connected by operators. Each operand is evaluated to amathematical sequence of p-tuples (the set interpretation). Amathematical sequence is an ordered list of elements that allowsduplicate members. The operators between each operand define how tocombine two sequences. Thus, each expression can be interpreted as asingle sequence (the normalized set form), where each element in thesequence corresponds to a single row, column, or layer.

In some embodiments, the normalized set form generated in step 612 ismore formally defined as p-entries and p-tuples. The set interpretationof an operand is a finite (possibly empty) sequence of heterogeneousp-tuples. Each p-tuple in a set interpretation defines a row (or columnor layer) of visual table 720. In other words, each p-tuple maps to arow, a column, or a layer in visual table 720. A p-tuple is a finitesequence of p-entries. A single p-tuple defines a single row (or columnor layer). The entries of a p-tuple define the spatial encoding (axis)within the row and the selection criteria on the fact table of adatabase 558. A p-entry is an ordered “tag-value” pair where the tagdefines the meaning and possible values of the value member of the pair.A p-entry will be written as tag:value; e.g., field:Profit. A tag can bea field, constant, or field name, as discussed in further detail inSection 5.4. In some embodiments, the panes 722 of the row, column, orlayer to which an ordered set of tuples (p-tuple) is mapped are orderedwithin the row, column, or layer in visual table 720 in the same orderthat is presented in the p-tuple.

In summary, each axis of visual table 720 is defined by an expressionfrom visual specification 550 that has been rewritten in normalized setform. The cardinality of this normalized set determines the number ofrows (or columns or layers) along the axis, with the exception of whenthe normalized set is the empty sequence. In a preferred embodiment,when the normalized set is an empty sequence, a single row or column iscreated rather than zero rows or columns. Each p-tuple within thenormalized set defines a row. (or column or layer). The p-entries withineach p-tuple define both a selection criterion on the database 558 facttable, selecting tuples to be displayed in the row, and the spatialencoding in the row, defining the positions of the graphical marks usedto visualize the database tuples. More information on the setinterpretation is found in Section 5.4, below.

In some embodiments visual table 720 is presented as a web interface. Insome embodiments, all or portions of user interface module 546 are runand displayed on a remote user computer in order to facilitate thepresentation of visual table 720 as a web interface.

Step 616—partition query results into tuples corresponding to panes 722in visual table 720. In step 616 (FIG. 6, FIG. 11) visual interpretermodule 556 processes query results that are returned by data interpretermodule 552. These query results are referred to as tuples. In someembodiments of the present invention visual interpreter module 556performs the following algorithm:

201: x-set = compute normalized set form of x-axis expression 202: y-set= compute normalized set form of y-axis expression 203: z-set = computenormalized set form of z-axis expression 204: for each x-entry in x-set{ 205: for each y-entry in y-set { 206: for each z-entry in z-set { 207:p-lookup = new PaneLookupDescriptor(x-entry, y-entry, z-entry) 208:p-spec = The PaneSpecification that applies to p-lookup 209: create thepane graphic 210: create the primitive object for rendering tuples 211:create the encoding objects for the visual properties and add toprimitive 212: create the per-pane transform that sorts tuples intodrawing order 213: retrieve the data from the pane-data-cache usingp-lookup 214: bind the data from the pane-data-cache using p-lookup 215:bind the pane to the data }}}Lines 201 through 203 are performed in step 612 (FIG. 6). Lines 204through 206 are a triple “for” loop to individual consider each pane 722in visual table 720. For each pane 4 lines 207-214 are performed.

In lines 207 and 208, the pane specification for pane i is located. Thepane specification is ultimately derived from visual specification 550.The pane specification for pane i defines the mark, encodings, etc., forthe pane. In lines 209-212, the pane graphic of pane i is created usingthe pane specification that applies to pane i. In line 210, primitiveobjects for rendering tuples within pane i is created. An example of apane primitive object is a bar in a bar chart. In line 211, the encodingobjects for the visual properties of each respective primitive objectcreated in line 210 are created and added to the corresponding primitiveobjects. Exemplary encoding objects in the case of a bar are color andsize of the bar. In line 212, the per-pane transform that sorts tuplesinto drawing order is applied. In other words, the per-pane transform isused to describe how tuples will be displayed in pane i.

In line 213, the data for pane i is retrieved from pane-data-cache 557using p-lookup. In lines 214-215, the data (e.g., a subset of the set oftuples that were retrieved from a query of database 558) for pane i isbound to pane i. In this way, data from a query of database 558 is boundto visual table 720 by visual interpreter module 556.

In other words, in lines 209-212 a tuple in a subset of tuplesassociated with pane i is encoded as a graphical mark. In some instancesthe tuple in the subset of tuples comprises a field that is then mappedto a graphical attribute (e.g., a color, a value, a size, a shape, aphrase, or a symbol). In some embodiments the field is classified asquantitative or ordinal and (i) when the field is classified asquantitative, it is mapped to a first graphical attribute and (ii) whenthe field is classified as ordinal it is mapped to a second graphicalattribute. In some embodiments the field is classified as independent ordependent and (i) when the field is classified as independent, it ismapped to a first graphical attribute and (ii) when the field isclassified as dependent it is mapped to a second graphical attribute.The first and second attribute are each independently a color, a value,a size, a shape, a phrase or a symbol.

In some embodiments, the subset of tuples associated with pane i isdetermined by a selection function. In some embodiments, the selectionfunction uses an identity of a schema field that is present in themetadata of the database 558 characterized in step 602 to form thesubset of tuples. For example, the specification may assign all tuplesthat belong to a specific schema field type to pane i. In someembodiments, the selection function uses a relational operator (e.g., aselection operator or a grouping operator) to form the subset of tuplesassociated with pane i. Further, the ordering of rows and columns invisual table 720 can be controlled and filtered as well.

The algorithm described in lines 201 through 215 assumes that each queryof 558 is available in a pane-data-cache 557. Recall that an importantadvantage of the present invention is that queries are typically groupedacross several panes. Thus, queries need to be partitioned into aseparate table for each pane and then placed in the pane-data-cache 557.While the present invention imposes no limitation on which softwaremodule performs this grouping transformation, in one embodiment of thepresent invention, the grouping transformation is performed by datainterpreter module 552 as part of a generalized algorithm for queryingdatabases 558. See, for example, the algorithm described in step 606,above.

In some embodiments of the present invention, step 608 returns a set oftuples. Next, in step 610 a new tuple is derived from the set of tuples.This new tuple is then incorporated into the set of tuples for possibleassociation with one or more panes 722 in the graphic that is specifiedby visual specification 550. In some instances a relational operator(e.g., a sorting operator, an aggregation operator, or a transformingoperator) is used to create the new tuple. An example of this is anadditional transformation that is performed to augment the querylanguage. For example, it is known that an MDX query can easilyaggregate all twelve months of a year into year total and then, say,aggregate multiple years into a multi-year total because thisaggregation occurs up and down the hierarchy. But MDX cannot easilyaggregate across a hierarchy (e.g., the totals for all Januariesregardless of the year). The present invention allows for aggregationacross a hierarchy by applying one or more local transformations to aset of returned tuples (e.g., a set of tuples returned from one or moreMDX queries). For example, in order to obtain totals for all Januariesregardless of year, one or more MDX queries are made to obtain therelevant tuples and then the month of January is aggregated acrossrespective years in the MDX query results.

In some embodiments of the present invention, step 608 returns a set oftuples. A group is formed using all or a portion of the tuples in theset of tuples. Then a graphic based on the group is formed. Suchembodiments are useful in instances where a multi-pane graphic isconstructed. Examples of such graphics include a line that connects eachtuple in a group or an area that encloses each tuple in the group.

In some embodiments, specification 550 organizes one or more panes 722into a plurality of layers and each layer in the plurality of layers isassigned a tuple from a different database 558 that was characterized instep 602. In some embodiments, the specification 550 organizes one ormore panes 722 into a plurality of columns and a plurality of rows andeach column in the plurality of columns is assigned a tuple from adifferent database 558 that was characterized in step 602. In stillother embodiments, the specification organizes the one or more panesinto a plurality of columns and a plurality of rows and each row in theplurality of rows is assigned to a tuple from a different database 558that was characterized in step 602. In still further embodiments, thespecification organizes the one or more panes into a plurality of pagesand each page in the plurality of pages is assigned to a tuple from adifferent database 558 that was characterized in step 602.

An overview of the steps performed in accordance with one embodiment ofthe present invention has been provided. The invention is highlyadvantageous because it takes advantage of the underlying hierarchy ofone or more target database 558 in order to allow a user to moreefficiently explore databases 558. A user can rapidly drill downhierarchical layers within each target database 558. For example, in oneembodiment of the invention, the interface includes a “▾” icon 708-6(FIG. 7). When the user presses the “▾” icon 708-6, the user ispresented with a listing of all the levels of the dimension (includingdiverging levels in complex dimensional hierarchies in the targetdatabases). When a new level is selected, this is interpreted as a drillclown (or roll up) operation along that dimension and the current levelis automatically replaced with the selected level (with the samequalification). Thus, the present invention allows the user to rapidlymove between different levels of detail along a dimension, refining thevisual specification 550 as the user navigates. At each level, thepresent invention forms efficient database queries using the novel tablealgebra of the present invention. Another advantage of the presentinvention is that a subset of tuples associated with a pane in step 616can be used as a visual specification 550 in a new iteration of steps605 through 616. For example, the user can select one or more tuples inthe subset of the tuples associated with the pane as a basis for a newspecification. Then, steps 606 through 616 can be repeated using the newspecification. Still another advantage of the present invention is thateach specification 550 can be expressed in a form that can be stored forlater usage. Storage of specifications 550 allow for services such asthe bookmarking of favored specifications as well as support forspecification “undo” and “redo”. In a specification “undo”, for example,the specification 550 that was used in a previous instance of step 604is obtained and used to perform steps 606 through 616.

5.3 Illustrative Types of Database Hierarchy and Database Organization

The present invention provides visualization techniques for theexploration and analysis of multidimensional analytic data stored indatabases 558. One form of databases 558 is a data warehouse. Datawarehouses are typically structured as either relational databases ormultidimensional data cubes. In this section, aspects of relationaldatabases and multidimensional data cubes that are relevant to thepresent invention are described. For more information on relationaldatabases and multidimensional data cubes, see Berson and Smith, 1997,Data Warehousing, Data Mining and OLAP, McGraw-Hill, New York; Freeze,2000, Unlocking OLAP with Microsoft SQL Server and Excel 2000, IDG BooksWorldwide, Inc., Foster City, Calif.; and Thomson, 1997, OLAP Solutions:Building Multidimensional Information Systems, Wiley ComputerPublishing, New Yolk. In addition, it will be appreciated that in someembodiments database 558 does not have a formal hierarchical structure.In such embodiments, hierarchical structure for the database is derivedby analyzing the database using user interface module 544.

5.3.1 Data organization

Databases have typically been used for operational purposes (OLTP), suchas order entry, accounting and inventory control. More recently,corporations and scientific projects have been building databases,called data warehouses or large on line analytical processing (OLAP)databases, explicitly for the purposes of exploration and analysis. The“data warehouse” can be described as a subject-oriented, integrated,time-variant, nonvolatile collection of data in support of managementdecisions. The key aspect of the data warehouse is that it is arepository for analytic data rather than transactional or operationaldata. The data contained in the data warehouse usually representshistorical data, e.g., transactions over time, about some key interestof the business or project. This data is typically collected from manydifferent sources such as operational databases, simulations, datacollection tools (e.g., tqdump), and other external sources.

Data warehouses are built using both relational databases andspecialized multidimensional structures called data cubes. In thissubsection, the organization of the data within these databases, such asthe database schemas, the use of semantic hierarchies, and the structureof data cubes, is explained. In the next subsection, the differencebetween the organization of OLAP databases and OLTP databases isdescribed.

5.3.2 Relational Databases

Relational databases organize data into tables where each rowcorresponds to a basic entity or fact and each column represents aproperty of that entity. For example, a table may represent transactionsin a bank, where each row corresponds to a single transaction, and eachtransaction has multiple attributes, such as the transaction amount, theaccount balance, the bank branch, and the customer. The table isreferred to as a relation, a row as a tuple, and a column as anattribute or field. The attributes within a relation can be partitionedinto two types: dimensions and measures. Dimensions and measures aresimilar to independent and dependent variables in traditional analysis.For example, the bank branch and the customer would be dimensions, whilethe account balance would be a measure. A single relational databasewill often describe many heterogeneous but interrelated entities. Forexample, a database designed for a coffee chain might maintaininformation about employees, products, and sales. The database schemadefines the relations (tables) in a database, the relationships betweenthose relations, and how the relations model the entities of interest.

5.33 Hierarchical Structure

Most dimensions in a databases have a hierarchical structure. Thishierarchical structure can be derived from the semantic levels of detailwithin the dimension or generated from classification algorithms. Thesystems and methods of the present invention use these hierarchies toprovide tools that an analyst can use to explore and analyze data atmultiple levels of detail calculated from the fact table. For example,rather than having a single dimension “state”, a hierarchical dimension“location” that has three levels, one each for country, state, andcounty, can be used Then, the analyst can aggregate the measures ofinterest to any of these levels. The aggregation levels are determinedfrom the hierarchical dimension, which is structured as a tree withmultiple levels. The highest level is the most aggregated and the lowestlevel is the least aggregated. Each level corresponds to a differentsemantic level of detail for that dimension. Within each level of thetree, there are many nodes, with each node corresponding to a valuewithin the domain of that level of detail of that dimension. The treeforms a set of parent-child relationships between the domain values ateach level of detail. These relationships are the basis for aggregation,drill down, and roll up operations within the dimension hierarchy. FIG.9 illustrates the dimension hierarchy for a Time dimension. Simplehierarchies, like the one shown in FIG. 9, are commonly modeled using astar schema. The entire dimensional hierarchy is represented by a singledimension table. In this type of hierarchy, there is only one path ofaggregation. However, there are more complex dimension hierarchies inwhich the aggregation path can branch. For example, a time dimensionmight aggregate from Day to both Week and Month. These complexhierarchies are typically represented using the snowflake schema, asdescribed in Section 2, which uses multiple relations (tables) torepresent the diverging hierarchies.

5.3.4 Data Cubes

A data warehouse can be constructed as a relational database usingeither a star or snowflake schema and will provide a conceptual model ofa multidimensional data set. However, the typical analysis operationssuch as summaries and aggregations are not well supported by therelational model. The queries are difficult to write in languages suchas SQL and the query performance is not ideal. As a result, typically,the fact tables and dimension tables are not used directly for analysisbut rather as a basis from which to construct a multidimensionaldatabase called a data cube.

Each axis in the data cube corresponds to a dimension in the relationalschema and consists of every possible value for that dimension. Forexample, an axis corresponding to states would have fifty values, onefor each state. Each cell in the data cube corresponds to a uniquecombination of values for the dimensions. For example, if there are twodimensions, “State” and “Product”, then there would be a cell for everyunique combination of the two, e.g., one cell each for (California,Tea), (California, Coffee), (Florida, Tea), (Florida, Coffee), etc. Eachcell contains one value per measure of the data cube. So if productproduction and consumption information is needed, then each cell wouldcontain two values, one for the number of products of each type consumedin that state, and one for the number of products of each type producedin that state. FIG. 13 illustrates a data cube for a hypotheticalnationwide coffee chain data warehouse. Each cell in the data cubesummarizes all measures in the base fact table for the correspondingvalues in each dimension.

Dimensions within the data warehouse are often augmented with ahierarchical structure. The systems and methods of the present inventionuse these hierarchies to provide tools that can be used to explore andanalyze the data cube at multiple meaningful levels of aggregation. Eachcell in the data cube then corresponds to the measures of the base facttable aggregated to the proper level of detail. If each dimension has ahierarchical structure, then the data warehouse is not a single datacube but rather a lattice of data cubes, where each cube is defined bythe combination of a level of detail for each dimension (FIG. 14). InFIG. 14, the hierarchical structure of each dimension (time, product,location) defines the lattice of cubes. Within the lattice, each cube isdefined by the combination of a level of detail for each dimension. Thecubes at the bottom of the lattice contain the most detailed informationwhereas the cubes at the top of the lattice are the most abstract.

5.3.5 OLAP Versus OLTP

The previous section described how both relational databases and datacubes could be organized and used for analytical purposes (OLAP).Traditionally, however, relational databases have bemused for day-to-dayoperational purposes. These OLTP databases address different issues thanOLAP databases or data warehouses and, as a result, have schemes andusage patterns that are quite different. It is necessary to understandthe differences between these two types of databases in order tounderstand the issues affecting the design of OLAP visualization tools.

OLTP databases are optimized for performance when processing shorttransactions to either query or modify data, possibly interfacing withmore then one system and supporting many simultaneous connections.Furthermore, query performance is typically secondary to issues likeavoiding data redundancy and supporting updates. Typical OLTP queriesretrieve a few dozen tuples from only a few relations and then updatesome of the triples. For example, a typical query might retrieve asingle customer's record based on their account number, or add a singletransaction to a sales relation when a sale occurs. Database schemadefinitions for operational databases focus on maximizing concurrencyand optimizing insert, update, and delete performance. As a result, theschema is often normalized, resulting in a database with many relations,each describing a distinct entity set.

In contrast, rather than being used to maintain updateable transactiondata, users need to be able to interactively query and explore OLAPdatabases. The queries for OLAP are very different in that theytypically retrieve thousands of rows of information and modify none ofthem. The queries are large, complex, ad hoc, and data-intensive.Because an operational schema separates the underlying data into manyrelations, executing these analytical queries on a database based on anoperational schema would require many expensive join computations. Sinceanalysis databases are typically read-only, and because queryperformance is the primary concern, OLAP databases sacrifice redundancyand update performance to accelerate queries, typically by denormalizingthe database into a very small number of relations using a star orsnowflake schema. External tools can typically view an OLAP database aseither a data cube or a single large relation (table).

5.3.6 Multidimensional Analysis Operations

In some embodiments database 558 is typically quite large, consisting ofmany dimensions each with hierarchical structure and often many members.To navigate the resulting lattice of data cubes and perform dimensionalreduction to extract data for analysis, there are a number ofmultidimensional analysis operations that are used. This sectiondescribes such operations.

Drill down refers to the process of navigating through the lattice ofdata cubes in the direction of more detail. It is the technique used tobreak one piece of information into smaller and more detailed parts.Roll up is the inverse of drill down, aggregating detailed data intocoarser elements. Projection (illustrated in FIG. 15) reduces thedimensionality of an n-dimensional data cube to (n−1) by aggregatingacross a dimension. For example, in FIG. 15, the first projectionsummarizes across “Location”, reducing the 3-dimensional cube to a2-dimensional cube.

Where projection reduces dimensionality via aggregation, slicing(illustrated in FIG. 16) reduces dimensionality by filtering a dimensionto a single value. In other words, one dimension is held constant togenerate a slice across that dimension. In the example illustrated inFIG. 16, a 2-dimensional slice corresponding to data for “Qtr 2” hasbeen taken from the “Time” dimension.

5.3.7 Data Characterization for Visualization

Having described how the OLA data used by some embodiments of thepresent invention is organized, additional data characterization used tosupport some visualization processes of the present invention is nowdiscussed. For the purposes of visualization, more about an attributethan is usually captured by a database system is needed. Databasestypically provide limited information about a field, such as its name,whether a field is a dimension or measure, and its type (e.g., time,integer, float, character).

In some embodiments of the present invention, a determination is made asto whether a database field (operand) is nominal, ordinal, orquantitative in order to determine how to encode the field in a visualtable using visual properties. Representative visual properties include,but are not limited to, color, size, or position. This characterizationis based on a simplification of Stevens' scales of measurement. SeeStevens, On the theory of scales of measurement, In Science, (103), pp.677-680. In some embodiments, this characterization is furthersimplified depending on if the context emphasizes the difference betweendiscrete data and continuous data or if the context emphasizes whetherthe field has an ordering. In one example, when encoding a fieldspatially, the emphasis is on whether a field has discrete values.Furthermore, when a field is assigned to an axis, it has an ordering.Thus, in this context, nominal fields that do not normally have anordering are assigned one and then treated as an ordinal field in someembodiments of the present invention. The resulting characterization iscalled categorical. In contrast, when assigning visual properties suchas color to a field, then the important distinguishing characterizationis order. In this context, the ordinal and quantitative fields aretreated as a single characterization and nominal fields are consideredseparately, in some embodiments of the present invention. In addition,attributes have associated units and semantic domains. For example,attributes encode time, geographic units such as latitude, or physicalmeasurements. If this information is available, it can also be used togenerate more effective visual encodings and aid in determining thegeometry (e.g., aspect ratio) of a visual table 720. For example,knowing that the x and y axis of a visual table 720 correspond tolatitude and longitude, rather than profit and sales, will affect thedetermination of the appropriate geometry.

Databases also typically only store the current domain of a field—thevalues that currently exist within the database—without any ordering.However, for analysis it is important to understand the actual domain ofa field, such as the possible values and their inherent (if applicable)ordering. To encode an attribute as an axis of a visual table 720, allpossible values and their ordering need to be determined so that anindication of when data is missing can be made and to present datawithin its semantic context rather than using some arbitrary ordering,e.g., alphabetic. In some embodiments, this additional datacharacterization is captured in an attributed file 580 (e.g., an XMLdocument) that is associated with database 558 (FIG. 5).

5.4 Algebra

As discussed above, a complete table configuration consists of threeseparate expressions. Two of the expressions define the configuration ofthe x- and y-axes of a visual table 720, partitioning the table intorows and columns. The third expression defines the z-axis of visualtable 720, which partitions the display into layers of x-y tables thatare composited on top of one another. This section sets forth analgebra, including its syntax and semantics, that is used in these threeexpressions in some embodiments of the present invention. As discussedabove, each expression in the algebra used in some embodiments of theinvention is composed of operands connected by operators. Operands andoperators will be discussed in turn in the following sections.

5.4.1 Operands

The operands in the table algebra described in this section are thenames of the fields (field operands) of the database 558 and the namesof predefined constant sequences of p-tuples (constant operands). Insome embodiments, the categorization of field types is reduced toordinal and quantitative by assigning a default alphabetic ordering toall nominal fields and then treating them as ordinal. Thus, in suchembodiments, there are three classes of operands: (1) ordinal fieldoperands, (2) quantitative field operands, and (3) constant operands.Throughout the remainder of this section, the terms A and B representordinal field operands, P and Q represent quantitative field operands, Crepresents a constant operand, and X, Y, and Z represent expressions.

5.4.1.1 Set Interpretations

Set interpretations are assigned to each operand symbol in the followingmanner. Ordinal fields are assigned the members of the ordered domain ofthe field. Quantitative fields are assigned the single element setcontaining the field name. Constant operands are assigned theirpredefined set interpretation.A=domain(A)=<(A:a), . . . ,(A:a _(n))>P=<(field:P)>C=<(constant:c), . . . ,(constant:c _(m))>

For simplicity of exposition, tags are not included in the remaining setinterpretations within this section except where necessary.

The assignment of sets to field operands reflects the difference in howthe two types of fields will be encoded in the structure of visualtables 720. Ordinal fields partition visual table 720 (and the databasetuples) into rows and columns, whereas quantitative fields are spatiallyencoded as axes within panes 722.

5.4.1.2 Constant Operands

Constant operands define neither selection criteria nor spatialencodings. Instead, they can be used to generate additional rows withoutpartitioning database tuples. This facilitates the layering ofheterogeneous databases. In some embodiments, constant operands aretreated as ordinal field operands by defining a virtual fact table andthen defining operators relative to this virtual fact table. Let (C, . .. , C_(n)) be a set of constant operands, R_(C) be a relation with asingle attribute (C_(j)) whose domain corresponds to the predefined setinterpretation of and C_(i), and FT′ be the fact table for database 558.The virtual fact table VFT is defined relative to the given set ofconstant operands as:VFT=FT×Rc _(i) . . . ×Rc _(i)This algebra contains one predefined constant operand, the emptysequence.

5.4.1.3 Filtering and Sorting of Field Operands

Ifs field is to be filtered (or sorted), the filtered and sorted domainis listed directly after the field operand in the expression, in effectspecifying a set interpretation for the operand. Given an ordinal fieldA with domain (A)=<(a), . . . , (a_(n))>, the operand can be filteredand sorted within an expression by stating the filtered and sorteddomain (<b, >, . . . , b_(j)>, b_(i)εdomain (A)) directly after theordinal operand and the set interpretation is the listed domain:A[b, . . . ,b _(j)]=<(b), . . . ,(b _(j))>Similarly, a filtered domain can be specified for a quantitative fieldby listing the minimum and maximum values of the desired domain. Thisinformation is included in the generated set interpretation:P[min,max]=<(field:P[min,max])>Having defined the operands and the generation of their setinterpretations, the four operators in the algebra of the presentinvention can be defined.

5.4.2 Operators

As stated above, a valid expression in the algebra is an orderedsequence of one or more operands with operators between each pair ofadjacent operands. The operators in this algebra, in order ofprecedence, are dot (.), cross (x), nest (/), and concatenation (+).Parentheses can be used to alter precedence. Because each operand isinterpreted as a sequence, the precise semantics of each operator isdefined in terms of how it combines two sequences (one each from theleft and right operands) into a single sequence. Definitions of the dot,cross, nest and concatenation operators are provided below. The exactdefinitions provides below are merely exemplary and other definitionsthat are consistent with the features of each operator are within thescope of the present invention.

5.4.2.1 Concatenation

The concatenation operator performs an ordered union of the setinterpretations of the two operands and can be applied to any twooperands or expressions:

$\begin{matrix}{{A + B} = {\left\langle {(a),\ldots\mspace{14mu},\left( a_{n} \right)} \right\rangle\bigcup\left\langle {(b),\ldots\mspace{14mu},\left( b_{m} \right)} \right\rangle}} \\{{= \left\langle {(a),\ldots\mspace{14mu},\left( a_{n} \right)} \right\rangle},\left\langle {(b),\ldots\mspace{14mu},\left( b_{m} \right)} \right\rangle}\end{matrix}$ $\begin{matrix}{{P + Q} = {\left\langle (P) \right\rangle\bigcup\left\langle (Q) \right\rangle}} \\{= \left\langle {(P),(Q)} \right\rangle}\end{matrix}$ $\begin{matrix}{{A + P} = {\left\langle {(a),\ldots\mspace{14mu},\left( a_{n} \right)} \right\rangle\bigcup\left\langle (P) \right\rangle}} \\{= \left\langle {(a),\ldots\mspace{14mu},\left( a_{n} \right),(P)} \right\rangle}\end{matrix}$ $\begin{matrix}{{P + A} = {\left\langle (P) \right\rangle\bigcup\left\langle {(a),\ldots\mspace{14mu},\left( a_{n} \right)} \right\rangle}} \\{= \left\langle {(p),(a),\ldots\mspace{14mu},\left( a_{n} \right)} \right\rangle}\end{matrix}$ $\begin{matrix}{{X + Y} = {\left\langle {\left( {x,\ldots\mspace{14mu},x_{i}} \right),\ldots\mspace{14mu},\left( {x_{j},\ldots\mspace{14mu},x_{ik}} \right)} \right\rangle\bigcup}} \\{\left\langle {\left( {y,\ldots\mspace{14mu},y_{m}} \right),\ldots\mspace{14mu},\left( {y_{n},\ldots\mspace{14mu},y_{no}} \right)} \right\rangle} \\{= \left\langle \begin{matrix}{\left( {x,\ldots\mspace{14mu},x_{i}} \right),\ldots\mspace{14mu},\left( {x_{j},\ldots\mspace{14mu},x_{ik}} \right),} \\{\left( {y,\ldots\mspace{14mu},y_{m}} \right),\ldots\mspace{14mu},\left( {y_{n},\ldots\mspace{14mu},y_{no}} \right)}\end{matrix} \right\rangle}\end{matrix}$

The only algebraic property that holds for the concatenation operator isassociatively:(X+Y)+Z=(<(x, . . . ,x _(i)), . . . ,(x _(j) , . . . ,x _(ik))>∪<(y, . . . ,y_(m)), . . . ,(y _(n) , . . . ,y _(no))>)∪<(z, . . . ,z _(p)), . . . ,(z _(q) , . . . ,x _(qr))>=<(x, . . . ,x _(i)), . . . ,(x _(j) , . . . , x _(ik))>∪(<(y, . . . ,y _(m)), . . . ,(y _(n) , . . . ,y _(no))>∪<(z, . . . ,z_(p)), . . . ,(z _(q) , . . . ,x _(qr))>)=X+(Y+Z)

The concatenation operator is not commutative because the ordered unionof two sequences is not commutative.

5.4.2.2 Cross

The cross operator performs a Cartesian product of the sets of the twosymbols:

$\begin{matrix}{\mspace{79mu}{{A \times B} = {\left\langle {(a),\ldots\mspace{14mu},\left( a_{n} \right)} \right\rangle \times \left\langle {(b),\ldots\mspace{14mu},\left( b_{m} \right)} \right\rangle}}} \\{= \left\langle {\left( {a,b} \right),\ldots\mspace{14mu},\left( {a,b_{m}} \right),\ldots\mspace{14mu},\left( {a_{n},b} \right),\ldots\mspace{14mu},\left( {a_{n},b_{m}} \right)} \right\rangle}\end{matrix}$ $\begin{matrix}{\mspace{79mu}{{A \times P} = {\left\langle {(a),\ldots\mspace{14mu},\left( a_{n} \right)} \right\rangle \times \left\langle (P) \right\rangle}}} \\{= \left\langle {\left( {a,P} \right),\ldots\mspace{14mu},\left( {a_{n},P} \right)} \right\rangle}\end{matrix}$ $\begin{matrix}{{X \times Y} = {\left\langle {\left( {x,\ldots\mspace{14mu},x_{i}} \right),\ldots\mspace{14mu},\left( {x_{j},\ldots\mspace{14mu},x_{ik}} \right)} \right\rangle \times}} \\{\left\langle {\left( {y,\ldots\mspace{14mu},y_{m}} \right),\ldots\mspace{14mu},\left( {y_{n},\ldots\mspace{14mu},y_{no}} \right)} \right\rangle} \\{= \left\langle \begin{matrix}{\left( {x,\ldots\mspace{14mu},x_{i},y,\ldots\mspace{14mu},y_{m}} \right),\ldots\mspace{14mu},\left( {x,\ldots\mspace{14mu},x_{i},y_{n},\ldots\mspace{14mu},y_{no}} \right)} \\\ldots \\{\left( {x_{j},\ldots\mspace{14mu},x_{ik},y,\ldots\mspace{14mu},y_{m}} \right),\ldots\mspace{14mu},\left( {x_{j},\ldots\mspace{14mu},x_{jk},y_{n},\ldots\mspace{14mu},y_{no}} \right)}\end{matrix} \right\rangle}\end{matrix}$

Quantitative fields and expressions may appear only as right-hand sideoperands when the cross operator is applied. The cross operator is alsoassociative but not commutative (because the ordered Cartesian productis not commutative):(X×Y)×Z=(<(x, . . . ,x _(i)), . . . ,(x _(j) , . . . ,x _(ik))>x<(y, . . . ,y_(m)), . . . ,(y _(n) , . . . ,y _(m)), . . . ,(y _(n) , . . . ,y_(no))>)×<(z, . . . ,z _(p)), . . . ,(z _(q) , . . . ,x _(qr))>=<(x, . . . ,x _(i)), . . . ,(x _(j) , . . . ,x _(ik))>×(<(y, . . . ,y _(m)), . . . ,(y _(n) , . . . ,y _(no))>×<(z, . . . ,z_(p)), . . . ,(z _(q) , . . . ,x _(qr))>)=X+(Y×Z)

5.4.2.3 Nest

The nest operator is similar to the cross operator, but it only createsset entries for which there exist database tuples with the same domainvalues. If VFT is defined to be the virtual fact table of the databasebeing analyzed relative to all constant operands in the expressions Xand Y, t to be a tuple, and t(X . . . X_(n)) to be the values of thefields X through X_(n) for the tuple t, then the nest operator can bedefined as follows:

$\begin{matrix}{{A/B} = \left. \left\langle {a,b} \right) \middle| {\exists{t \in {{VFT}\;{st}}}} \right.} \\\left. {= {{{{{{\left( {(a) \in A} \right)\&}\left( {{t(A)} = a} \right)}\&}\left( {(b) \in B} \right)}\&}\left( {{t(B)} = b} \right)}} \right\rangle\end{matrix}$ $\begin{matrix}{{X/A} = \left\langle \left( {x,\ldots\mspace{14mu},x_{n},a} \right) \middle| {\exists{t \in {{VFT}\;{st}}}} \right.} \\{= {{{\left( {\left( {x,\ldots\mspace{14mu},x_{n}} \right) \in X} \right)\&}\left( {{t\left( {X,\ldots\mspace{14mu},X_{n}} \right)} = \left( {x,\ldots\mspace{14mu},x_{n}} \right)} \right)}\&}} \\\left. \left. {= {{\left( {(a) \in A} \right)\&}\left( {{t(A)} = a} \right)}} \right) \right\rangle\end{matrix}$ $\begin{matrix}{{A/Y} = \left. \left\langle {a,y,\ldots\mspace{14mu},y_{m}} \right) \middle| {\exists{t \in {{VFT}\;{st}}}} \right.} \\{= {{{\left( {(a) \in A} \right)\&}\left( {{t(A)} = (a)} \right)}\&}} \\\left. {= {{\left( {\left( {y,\ldots\mspace{14mu},y_{m}} \right) \in Y} \right)\&}\left( {{t\left( {Y,\ldots\mspace{14mu},Y_{m}} \right)} = \left( {y,\ldots\mspace{14mu},y_{m}} \right)} \right)}} \right\rangle\end{matrix}$ $\begin{matrix}{{X/Y} = \left\langle \left( {x,\ldots\mspace{14mu},x_{n},y,\ldots\mspace{14mu},y_{m}} \right) \middle| {\exists{t \in {{VFT}\;{st}}}} \right.} \\{= {{\left( {\left( {x,\ldots\mspace{14mu},x_{n}} \right) \in X} \right)\&}\left( {{t\left( {X,\ldots\mspace{14mu},X_{n}} \right)} = {\left( \left( {x,\ldots\mspace{14mu},x_{n}} \right) \right)\&}} \right.}} \\{= {{\left( {\left( {y,\ldots\mspace{14mu},y_{m}} \right) \in Y} \right)\&}\left( {{t\left( {Y,\ldots\mspace{14mu},Y_{m}} \right)} = \left( \left( {y,\ldots\mspace{14mu},y_{m}} \right) \right)} \right\rangle}}\end{matrix}$

The ordering of the p-tuples in a sequence generated by application ofthe nest operator is the same as it would be in the sequence generatedby the application of the cross operator to the same operands.

The intuitive interpretation of the nest operator is “B within A”. Forexample, given the fields Quarter and Month, the expressionQuarter/Month would be interpreted as those months within each quarter,resulting in three entries for each quarter (assuming data exists forall months in the fact table). In contrast Quarter×Month would result in12 entries for each quarter. The nest operator may only be applied toordinal operands and expressions. Nest is an associative operator.

5.4.2.4 Dot

The cross and nest operators provide tools for generating ad hoccategorical hierarchies. However, data warehouses often containdimensions with explicit semantic hierarchies. The dot operator providesa mechanism for exploiting these hierarchical structures in the algebraof the present invention. The dot operator is similar to the nestoperator but is “hierarchy-aware”.

If DT is defined to be a relational dimension table defining a hierarchythat contains the levels A and B, and A precedes B in the schema of DT,then:A·B=<(a,b)|∃tεDTstt(A)=a&t(B)=b>

Similarly, dot can be defined relative to an expression X involving onlythe dot operator and levels from the same dimension hierarchy. DT isdefined to be the relational dimension table defining the dimension thatcontains all levels in X and the dimension level A. In addition, alllevels in X must appear in the schema of DT in the order they appear inX and they must precede A in the schema of DT. Then:X·A=<(x, . . . ,x _(n) ,a)|∃tεDTstt(X . . . X _(n))=(x, . . . , x_(n))&t(A)=a>The dot operator is also associative but not commutative.

Nest could be used for drilling down into a hierarchy but this usagewould be flawed. The nest operator is unaware of any definedhierarchical relationship between the dimension levels; instead, itderives a relationship based on the topics in the fact table. Not onlyis this approach inefficient, as fact tables are often quite large, butit can also yield incorrect results. For example, consider the situationwhere no data was logged for November. Application of the nest operatorto Quarter and Month would result in an incorrectly derived hierarchythat did not include November as a child of Quarter 4.

The dot operator provides a particularly advantageous method for workingwith database 558 hierarchy. This is because the dot operator uses thehierarchical information that is either (i) defined in database 558dimension tables or (ii), in instances where database 558 does not havedimension tables, is constructed by database hierarchy module 544 (withpossible user intervention). In contrast, the nest operator is unawareof the defined hierarchical relationship between dimension levels and/orthe hierarchy that is constructed by database hierarchy module 542.Instead, the nest operator works by deriving hierarchical typerelationships within the database based on existence scans of tuples indatabase 558 fact tables. This is an inefficient way of derivinghierarchical information because the fact tables can be quite large.Advantageously, the dot operator does not derive hierarchical typerelationships within the database based on existence scans. Rather, thedot operator uses the metadata associated with the database 558 thatdefines the database hierarchy. The form of this metadata will bedependent upon the exact nature of databases 558. In some instances themetadata will comprise, for example a star schema. In instances wherethe database 558 does not have such defined hierarchical relationships(for example in the case where database 558 is a flat file) the metadatawill be constructed by database hierarchy module.

5.4.2.5 Summary

Using the above set semantics for each operator, every expression in thealgebra can be reduced to a single set with each entry in the set beingan ordered p-tuple. We call this set evaluation of an expression thenormalized set form. The normalized set form of an expression determinesone axis of the table: the table axis is partitioned into columns (orrows or layers) so that there is a one-to-one correspondence between setentries in the normalized set and columns. FIG. 8 illustrates the axisconfigurations resulting from several expressions.

5.4.3 Algebraic Properties

In the present invention, an algebraic expression is interpreted as aset for two purposes (i) to determine the underlying tabular structureof a visual table 720 and (ii) to determine the tuples to be retrievedfrom database 558. In the former case, the ordering of the p-tuples inthe normalized set form is meaningful because it determines the orderingof the columns, rows, and layers of visual table 720. As a result, theonly algebraic property that holds for our operators is associativity.Commutative or distributive operators would allow algebraicmanipulations that change the ordering of the normalized set form.However, when performing interpretation to determine which databasetuples to retrieve, these constraints on the properties of the operatorscan be relaxed since the ordering of the p-tuples in the setinterpretation is not meaningful in the context of database queries.Specifically, for this purpose only, the set interpretations is treatedas bags instead of sequences (thus discarding ordering) and allow thefollowing algebraic properties:

Associative(A+B)+C=A+(B+C)(A·B)·C=A·(B·C)(A×B)×C=A×(B×C)(A/B)/C=A/(B/C)

DistributiveA×(B+C)=(A×B)+(A×C)A/(B+C)=(A/B)+(A/C)

CommutativeA+B=B+AA×B=B×AA/B=B/AIf the operators are changed to allow these algebraic properties, thencan be used to quickly determine the database queries or data cubeprojections required to generate a visual table 720.

5.4.4 Syntax Revisited

In the previous sections, the syntax of an algebra in accordance withthe present invention was defined as a sequence of operands separated byoperators. Some constraints on the applications of the operators wasalso provided. In this section, the syntax is made precise by using agrammar. To define a grammar, four things are define: a set of terminalsymbols, a set of non-terminals, a set of production rules, and a startsymbol. As such, the grammar in accordance with the present inventionhas ten terminal symbols:

Symbol Definition q_(field) The name of a quantitative field o_(field)The name of an ordinal field q_(dim) The name of a quantitativedimensional level O_(dim) The name of an ordinal dimensional level C Aconstant operand · × / + The operators of the algebra ( ) Parentheses

The following are the production rules for the grammar (E is the startsymbol):

E → O_(expr) | Q_(expr) O_(expr) → O_(expr) | O_(expr) + O_(expr) |O_(expr) × O_(expr) | O_(expr)/O_(expr) | O Q_(expr) → (Q_(expr)) | E +Q_(expr) | Q_(expr) + E | (O_(expr) × Q_(expr) | Q O → O_(hier) |o_(field) | c O_(hier) → O_(hier) · o_(dim) | o_(dim) Q → O_(hier) |q_(field) Q_(hier) → O_(hier) · q_(dim) | q_(dim)

The following are the main syntactic constraints on the operators thatare expressed in this grammar:

Cross: Quantitative operands, or expressions containing quantitativeoperands, can only be right-hand side operands of the cross operator.

Nest: The nest operator can only be applied to ordinal operands orexpressions.

Dot: The dot operator can only be applied to dimension levels.Furthermore, a quantitative field can only appear as the right-mostoperand of a dot operator, since quantitative dimension levels are onlypossible as the leaf level of a dimension hierarchy.

Concatenate: Concatenate can be applied to any operand.

Thus far, how the algebraic expressions partition tables into rows andcolumns has been discussed. How the algebraic handle layers will now bediscussed.

5.4.5 Layers

In the present invention a layer in a visual table 720 is a single x-ytable whose structure is defined by the x- and y-axes expressions. Everylayer in a specification is composited together back-to-front to formthe final visualization. A single visualization can combine multipledata sources. Each data source is mapped to a distinct layer or set oflayers. While all data sources and layers share the same configurationfor x- and y-axes of the table, each data source can have a differentexpression (the z-axis) for partitioning its data into layers. Layeringof multiple data sources and the partitioning of layers are illustratedin FIG. 17. In some embodiments of the present invention, each datasource in a visualization is mapped to a distinct layer. The layers fora data source can be partitioned into additional layers by the z-axisexpression for that data source. All the layers in a specification arecomposited together back-to-front to form the final visualization.

Constant operands are an important aspect of layering. A singlevisualization may be composed of multiple heterogeneous databases 558,each mapped to a distinct layer, and all layers must share the sameexpressions for the x- and y-axes. However, sometimes it is desirable toinclude ordinal fields in the x- and y-axes expressions that exist inonly a subset of the visualized databases. When this occurs, constantoperands are generated for the other layers with a predefined setinterpretation that matches the domain of the ordinal field in the layerin which the field does appear, Thus, the expressions can be properlyevaluated for each layer.

The z-axis expression for a data source is more constrained than theexpressions for the x and y-axes. Specifically, since layering must bediscrete, a z-axis expression can contain only ordinal operands; notquantitative operands. In other words, a z-axis expression isconstrained to the O_(expr) production rule in the grammar of thepresent invention.

5.4.6 Summary

The algebra of the present invention provides a succinct yet powerfulnotation for describing the underlying structure of visual tables 720.The algebraic expressions define how the table is partitioned into rows,columns, and layers, and additionally defines the spatial encodingswithin each pane 722 of the table.

At this point, it is useful to consider the conceptual data flow. Aswell as defining visual table 720 structure, the algebraic expressionsof the visual specification (formed on shelves 708-1, 708-4, and 708-5)define which tuples of the database 558 should be selected and mappedinto each pane 722. When a specification is interpreted, one or morequeries are generated to retrieve topics from the database (FIG. 6, step608; FIG. 18, step 1802). The resulting topics are partitioned intolayers and panes (FIG. 18, step 1804). Then, tuples within each pane aregrouped, sorted and aggregated (FIG. 18, step 1806). Once the tupleshave been sorted into panes 722, they are then mapped to graphic marksto generate a perceivable display (FIG. 18, step 1808).

5.5 Exemplary Visual Specification

To understand the advantages of the dot operator, the problems thatdimension levels create will be explained. Consider the Monthdimensional level in the time hierarchy illustrated in FIG. 9. Onepossible way to evaluate the Month dimensional level would be to listeach node value, including its path to the root for uniqueness, orderedby depth first traversal of the dimension hierarchy; e.g.,{1998.Qtr1.Jan, . . . , 1999.Qtr4,Dec}. Although this approach providesa unique set interpretation for each dimensional level, it limits theexpressiveness of the algebra. Any visual table 720 constructed toinclude Month must also include Year. It is not possible to createdisplays that summarize monthly values across years, a useful view thatshould be supported in a robust system. Interestingly, however,summarizing monthly values across years is not a standard projection ofa hierarchical database, such as a datacube, as it requires aggregatingacross a hierarchical level.

The solution to the problem of how to reduce a dimensional level to asingle set is the dot (“.”) operator. If DT is defined to be thedimensional table defining the hierarchy that contains the levels A andB, and A precedes B in the schema of DT, then:A·B={(a·b)|∃rεDTstA(r)=a&B(r)=b}where r is a record and A(r) is the value of operand A for record r.Thus, the dot produces a set of single-valued tuples, each containing aqualified value. If the two operands are not levels of the samedimension hierarchy (or set interpretations of operations on levels ofthe same hierarchy), or A does not precede B in the schema of DT (e.g.,A must be an ancestor level in the tree defined by DT), then the dotoperator evaluates to the empty set. With this definition, the twoexpressions “Month” and “Year.Month” are not equivalent “Month” isinterpreted as {Jan, Feb, . . . , Dec} whereas “Year.Month” isinterpreted as {1998.Jan, 1998.Feb, . . . , 1999.Dec}. With a fullypopulated fact table, Year.Month is equivalent to Year/Month (where “/”is the nest operator defined in Section 5.4).

5.6 Examples

Each of the following examples demonstrates how database analysis canprogress from a high level of abstraction to detailed views of the data.Furthermore, each example shows the importance of being able to easilychange the data being viewed, pivot dimensions, and drill down databasehierarchy during the analysis process.

5.6.1 Example 1 Mobile Network Usage

FIG. 19 shows an analysis of a 12-week trace of every packet thatentered or exited the mobile network in the Gates building at StanfordUniversity. For more information on this trace, see Tang and Baker,“Analysis of a Local-Area Wireless Network”, Proc. of the 6^(th)International Conference on Mobile Computing and Networking, August2000, pp. 1-10. Over the 12 weeks, 78 million packet headers werecollected. The analysis goal is to understand usage patterns of themobile network. This data is stored in a data cube with many differentdimensions (User, Time, Remote host, Traffic direction, andApplication), each with multiple levels of detail. In this analysis, thequeries generated when the user dropped a field on a shelf took one totwo seconds to execute and returned several hundred to tens of thousandsof tuples.

To start the analysis, the analyst first sees if any patterns in timecan be spotted by creating a series of line charts in FIG. 19A showingpacket count and size versus time for the most common applications,broken down and colored by the direction of the traffic. In thesecharts, the analyst can see that the web is the most consistently usedapplication, while session is almost as consistent. File transfer is theleast consistent, but also has some of the highest peaks in bothincoming and outgoing ftp traffic. Note the log scale on the y-axes.

Given this broad understanding of traffic patterns, the next questionposed by the analyst is how the application mix varies depending on theresearch area. The analyst pivots the display to generate a single linechart of packet count per research area over time, broken down andcolored by application class (FIG. 19B, where curve 1902 is web, curve1904 is session, and curve 1906 is ftp). From this breakdown, theanalyst can see that the graphics group was responsible for the largeincoming and outgoing file transfers and that the systems group badatypically high session traffic.

Curious, the analyst then drills down further to see the individualproject groups (FIG. 19C), discovering that the large file transferswere due to the rendering group within the graphics lab, while therobotics lab had vastly different behavior depending on the particulargroup (the mob group dominated by session traffic, while the learninggroup had more web traffic, for example).

5.6.2 Example 2 Year 2000 Presidential Election Results

FIG. 20 shows the systems and methods of the present invention beingused to explore and analyze the results of the 2000 presidentialelection. This data is particularly interesting because thevisualizations used to explore it are created from two separate datasets. The first data set is a relational database of approximately500,000 tuples (stored in Microsoft's SQLServer) describing detailedpolygonal outlines of the states and counties in the USA. Additionallevels of detail have been constructed by polygon simplification, andthe resulting levels of detail form a Location hierarchy. The seconddata set is stored as a data cube (in Microsoft's Analysis Server) andcontains detailed county-by-county vote results (also with a Locationdimension). In the first two visualizations (FIGS. 20A and 20B), thesedata sets are explicitly joined prior to analysis. In the finalvisualization (FIG. 20C) the ability of the present invention tovisually join data sets using layers is relied upon. In this analysis,the execution time for the queries varied from less than one second forthe overview visualizations to two seconds for the detailedvisualizations, where the retrieved relation included tens of thousandsof tuples.

In FIG. 20A, the analyst has generated an overview of the entire countryat the State level in the Location hierarchy, coloring each state bywhich candidate won that state. The analyst is interested in moredetailed results for the state of Florida, so the analysis filters onthe Latitude and Longitude measures to focus on Florida and changes thelevel of detail to County, generating FIG. 20B. In the finalvisualization, shown in FIG. 20C, the analyst further focuses on thesouthern tip of Florida (by again filtering the Latitude and Longitudemeasures). Furthermore, the analysis adds two additional layers to thevisualization (read directly from the data cube) and displays both thename and the total number of votes counted in each county.

5.6.3 Example 3 Historical Profit/Sales for a Coffee Chain

The example is illustrated in FIG. 21. The data being analyzed is twoyears of business metrics for a hypothetical nationwide coffee chain,comprising approximately 5,000 tuples stored in a data cube. The data ischaracterized by three main dimensions (Time, Products, and Location),each with multiple levels of detail. We consider a scenario where theanalyst is concerned with reducing marketing expenses and is trying toidentify products that are not generating profit and sales proportionalto their marketing costs. The typical query time for the visualizationscreated in this scenario was between 0.1 and 0.2 seconds.

The first visualization created, FIG. 21A, is an overview of three keymeasures (Profit, Sales, and Marketing) as a scatterplot matrix. Theanalyst has drilled down using the Level of Detail shelf to the Productand State level. The two circle charts circled that several of thedistributions do not reflect the positive correlations that the analystwas expecting. To further investigate, the analyst reduces thescatterplot matrix to two graphs and colors the records by Market andProducttype (FIG. 21B), thus identifying espresso products in the Eastregion and tea products in the West region (circled in FIG. 21B) ashaving the worst marketing cost to profit ratios.

In the final visualization, FIG. 21C, the analyst drills down into thedata to get a more detailed understanding of the correlations bycreating a small multiple set of stacked bar charts, one for each Marketand Producttype. Within each chart, the data is further drilled down byindividual Product and State. Finally, each bar is colored by theMarketing cost. As can be seen in the visualization, several productssuch as “Caffe Mocha” in the East have negative profit (a descendingbar) with high marketing cost. Having identified such poorly performingproducts, the analyst can modify the marketing costs allocated to them.

5.7 References Cited

All references cited herein are incorporated herein by reference intheir entirety and for all purposes to the same extent as if eachindividual publication or patent or patent application was specificallyand individually indicated to be incorporated by reference in itsentirety for all purposes.

5.8 Alternative Embodiments

The present invention can be implemented as a computer program productthat comprises a computer program mechanism embedded in a computerreadable storage medium. For instance, the computer program productcould contain the program modules shown in FIG. 5. These program modulesmay be stored on a CD-ROM, magnetic disk storage product, or any othercomputer readable data or program storage product. The software modulesin the computer program product can also be distributed electronically,via the Internet or otherwise, by transmission of a computer data signal(in which the software modules are embedded) on a carrier wave.

Many modifications and variations of this invention can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific embodiments described herein areoffered by way of example only, and the invention is to be limited onlyby the terms of the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A computer-implemented method, comprising: at acomputer having one or more processors and memory storing programsexecuted by the one or more processors: receiving a visual specificationfor use in conjunction with a multi-dimensional database; determiningone or more queries from the visual specification, wherein at least oneof the one or more queries relates to a level of a hierarchicaldimension of the database; constructing one or more visual tables fromthe visual specification; retrieving a plurality of tuples from thedatabase, wherein each tuple satisfies at least one of the one or morequeries; and for a retrieved tuple, generating a respective visual markwithin a respective one of the one or more visual tables.
 2. Thecomputer-implemented method of claim 1, wherein the visual specificationcomprises a plurality of expressions, each expression including one ormore operands, and wherein each of the expressions defines a respectiveaxis of a plurality of axes for the one or more visual tables.
 3. Thecomputer-implemented method of claim 2, wherein the plurality of axescomprise at least x and y axes.
 4. The computer-implemented method ofclaim 3, wherein at least one of the plurality of axes represents two ormore operands.
 5. The computer-implemented method of claim 2, wherein:at least one of the one or more visual tables includes a plurality ofpanes; the operands comprise measures and dimensions, and at least oneof the one or more visual tables is partitioned into rows and columnsbased on the dimensions, and axes within the panes are spatially encodedbased on the measures.
 6. The computer-implemented method of claim 5,wherein the visual specification defines an organization of theplurality of panes into a plurality of rows and a plurality of columns.7. The computer-implemented method of claim 2, further comprising:displaying a graphical user interface providing a plurality of shelves,each shelf associated with a respective axis of the plurality of axes;and enabling a user to input, via the graphical user interface, thevisual specification by associating each of the plurality of shelveswith one or more operands, thereby associating a corresponding axis ofthe plurality of axes with the one or more operands.
 8. Thecomputer-implemented method of claim 1, further comprising:constructing, from the visual specification, one or more algebraicexpressions that define how the one or more visual tables arepartitioned into rows, columns, and layers.
 9. The computer-implementedmethod of claim 8, wherein the algebraic expressions constructed fromthe visual specification further define spatial encodings for theplurality of the axes of the one or more visual tables.
 10. Thecomputer-implemented method of claim 1, wherein the hierarchicaldimension is associated with time and comprises a plurality of fieldscorresponding to different time divisions.
 11. The computer-implementedmethod of claim 10, wherein the different time divisions comprise two ormore of: day, week, month, quarter, and year.
 12. Thecomputer-implemented method of claim 1, wherein receiving the visualspecification comprises receiving the visual specification from a user.13. A system for interpreting a visual specification, comprising: acomputer having one or more processors and memory storing programsexecuted by the one or more processors, one or more of the programsbeing configured to: receive a visual specification for use inconjunction with a multi-dimensional database; determine one or morequeries from the visual specification, wherein at least one of the oneor more queries relates to a level of a hierarchical dimension of thedatabase; construct one or more visual tables from the visualspecification; retrieve a plurality of tuples from the database, whereineach tuple satisfies at least one of the one or more queries; and for aretrieved tuple, generate a respective visual mark within a respectiveone of the one or more visual tables.
 14. A non-transitory,computer-readable storage medium storing one or more programs forexecution by one or more processors of a computer system, the one ormore programs comprising instructions for: receiving a visualspecification for use in conjunction with a multi-dimensional database;determining one or more queries from the visual specification, whereinat least one of the one or more queries relates to a level of ahierarchical dimension of the database; constructing one or more visualtables from the visual specification; retrieving a plurality of tuplesfrom the database, wherein each tuple satisfies at least one of the oneor more queries; and for a retrieved tuple, generating a respectivevisual mark within a respective one of the one or more visual tables.15. The computer-readable storage medium of claim 14, wherein the visualspecification comprises a plurality of expressions, each expressionincluding one or more operands, and wherein each of the expressionsdefines a respective axis of a plurality of axes for the one or morevisual tables.
 16. The computer-readable storage medium of claim 15,wherein: at least one of the one or more visual tables includes aplurality of panes; the operands comprise measures and dimensions, andat least one of the one or more visual tables is partitioned into rowsand columns based on the dimensions, and axes within the panes arespatially encoded based on the measures.
 17. The computer-readablestorage medium of claim 16, wherein the visual specification defines anorganization of the plurality of panes into a plurality of rows and aplurality of columns.
 18. The computer-readable storage medium of claim14, wherein the one or more programs further comprise instructions forconstructing, from the visual specification, one or more algebraicexpressions that define how the one or more visual tables arepartitioned into rows, columns, and layers.
 19. The computer-readablestorage medium of claim 18, wherein the algebraic expressionsconstructed from the visual specification further define spatialencodings for the plurality of the axes of the one or more visualtables.
 20. The computer-readable storage medium of claim 14, whereinthe hierarchical dimension is associated with time and comprises aplurality of fields corresponding to different time divisions.